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Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations


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Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems (ADMS) can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of automation. In this article, we consider the feasibility and efficacy of ethics-based auditing (EBA) as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS.
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Science and Engineering Ethics (2021) 27:44
1 3
Ethics‑Based Auditing ofAutomated Decision‑Making
Systems: Nature, Scope, andLimitations
JakobMökander1 · JessicaMorley1 · MariarosariaTaddeo1,2 ·
Received: 18 February 2021 / Accepted: 8 June 2021 / Published online: 6 July 2021
© The Author(s) 2021
Important decisions that impact humans lives, livelihoods, and the natural environ-
ment are increasingly being automated. Delegating tasks to so-called automated
decision-making systems (ADMS) can improve efficiency and enable new solutions.
However, these benefits are coupled with ethical challenges. For example, ADMS
may produce discriminatory outcomes, violate individual privacy, and undermine
human self-determination. New governance mechanisms are thus needed that help
organisations design and deploy ADMS in ways that are ethical, while enabling
society to reap the full economic and social benefits of automation. In this article,
we consider the feasibility and efficacy of ethics-based auditing (EBA) as a gov-
ernance mechanism that allows organisations to validate claims made about their
ADMS. Building on previous work, we define EBA as a structured process whereby
an entity’s present or past behaviour is assessed for consistency with relevant prin-
ciples or norms. We then offer three contributions to the existing literature. First,
we provide a theoretical explanation of how EBA can contribute to good govern-
ance by promoting procedural regularity and transparency. Second, we propose
seven criteria for how to design and implement EBA procedures successfully. Third,
we identify and discuss the conceptual, technical, social, economic, organisational,
and institutional constraints associated with EBA. We conclude that EBA should be
considered an integral component of multifaced approaches to managing the ethical
risks posed by ADMS.
Keywords Artificial intelligence· Auditing· Automated decision-making· Ethics·
* Jakob Mökander
Extended author information available on the last page of the article
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J. Mökander etal.
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44 Page 2 of 30
Automated decision-making systems (ADMS), i.e. autonomous self-learning sys-
tems that gather and process data to make qualitative judgements with little or no
human intervention, increasingly permeate all aspects of society (AlgorithmWatch,
2019). This means that many decisions with significant implications for people and
their environments—which were previously made by human experts—are now made
by ADMS (Karanasiou & Pinotsis, 2017; Krafft etal., 2020; Zarsky, 2016). Exam-
ples of the use of ADMS by both governments and private entities include poten-
tially sensitive areas like medical diagnostics (Grote & Berens, 2020), recruitment
(Sánchez-Monedero etal., 2020), driving autonomous vehicles (Evans etal., 2020),
and the issuing of loans and credit cards (Aggarwal etal., 2019; Lee etal., 2020).
As information societies mature, the range of decisions that can be automated in this
fashion will increase, and ADMS will be used to make ever-more critical decisions.
From a technical perspective, the specific models used by ADMS vary from sim-
ple decision trees to deep neural networks (Lepri etal., 2018). In this paper, how-
ever, we focus not on the underlying technologies but rather on the common features
of ADMS from which ethical challenges arise. In particular, it is the combination
of relative autonomy, complexity, and scalability that underpin both beneficial and
problematic uses of ADMS (more on this in sectionAutomated Decision-Making
Systems). Delegating tasks to ADMS can help increase consistency, improve effi-
ciency, and enable new solutions to complex problems (Taddeo & Floridi, 2018).
Yet these improvements are coupled with ethical challenges. As noted already by
Wiener (1988 [1954]): “The machine, which can learn and can make decisions on
the basis of its learning, will in no way be obliged to make such decisions as we
should have made, or will be acceptable to us.” For example, ADMS may leave
decision subjects vulnerable to harms associated with poor-quality outcomes, bias
and discrimination, and invasion of privacy (Leslie, 2019). More generally, ADMS
risk enabling human wrongdoing, reducing human control, removing human respon-
sibility, devaluing human skills, and eroding human self-determination (Tsamados
etal., 2020).
If these ethical challenges are not sufficiently addressed, a lack of public trust in
ADMS may hamper the adoption of such systems which, in turn, would lead to sig-
nificant social opportunity costs through the underuse of available and well-designed
technologies (Cookson, 2018). Addressing the ethical challenges posed by ADMS
is therefore becoming a prerequisite for good governance in information societies
(Cath etal., 2018). Unfortunately, traditional governance mechanisms designed to
oversee human decision-making processes often fail when applied to ADMS (Kroll
etal., 2016). One important reason for this is that the delegation of tasks to ADMS
curtails the sphere of ethical deliberation in decision-making processes (D’Agostino
& Durante, 2018). In practice, this means that norms that used to be open for inter-
pretation by human decision-makers are now embodied in ADMS. From an ethical
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Ethics-Based Auditing of Automated Decision-Making Systems Page 3 of 30 44
perspective, this shifts the focus of ethical deliberation from specific decision-mak-
ing situations to the ways in which ADMS are designed and deployed.
From Principles toPractice
In response to the growing need to design and deploy ADMS in ways that are
ethical, over 75 organisations—including governments, companies, academic
institutions, and NGOs—have produced documents defining high-level guidelines
(Jobin et al., 2019). Reputable contributions include Ethically Aligned Design
(IEEE, 2019), Ethics Guidelines for Trustworthy AI (AI HLEG, 2019), and the
OECD’s Recommendation of the Council on Artificial Intelligence (OECD,
2019). Although varying in terminology, the different guidelines broadly con-
verge around five principles: beneficence, non-maleficence, autonomy, justice,
and explicability (Floridi & Cowls, 2019).
While a useful starting point, these principles tend to generate interpretations
that are either too semantically strict, which are likely to make ADMS overly
mechanical, or too flexible to provide practical guidance (Arvan, 2018). This
indeterminacy hinders the translation of ethics principles into practices and leaves
room for unethical behaviours like ‘ethics shopping’, i.e. mixing and matching
ethical principles from different sources to justify some pre-existing behaviour;
‘ethics bluewashing’, i.e. making unsubstantiated claims about ADMS to appear
more ethical than one is; and ‘ethics lobbying’, i.e., exploiting ethics to delay or
avoid good and necessary legislation (Floridi, 2019). Moreover, the adoption of
ethics guidelines remains voluntary, and the industry lacks both incentives and
useful tools to translate principles into verifiable criteria (Raji etal., 2020). For
example, interviews with software developers indicate that while they consider
ethics important in principle, they also view it as an impractical construct that
is distant from the issues they face in daily work (Vakkuri etal., 2019). Further,
even organisations that are aware of the risks posed by ADMS may struggle to
manage these, either due to a lack of useful governance mechanisms or conflict-
ing interests (PwC, 2019). Taken together, there still exists a gap between the
‘what’ (and ‘why’) of ethics principles, and the ‘how’ of designing, deploying,
and governing ADMS in practice (Morley etal., 2020).
A vast range of governance mechanisms that aim to support the translation
of high-level ethics principles into practical guidance has been proposed in the
existing literature. Some of these governance mechanisms focus on interventions
in the early stages of software development processes, e.g. by raising the aware-
ness of ethical issues among software developers (Floridi etal., 2018), creating
more diverse teams of software developers (Sánchez-Monedero et al., 2020),
embedding ethical values into technological artefacts through proactive design
(Aizenberg and van den Hoven 2020; van de Poel, 2020), screening potentially
biased input data (AIEIG, 2020), or verifying the underlying decision-making
models and code (Dennis etal., 2016). Other proposed governance mechanisms,
such as impact assessments (ECP, 2018), take the outputs of ADMS into account.
Yet others focus on the context in which ADMS operate. For example, so-called
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Human-in-the-Loop protocols imply that human operators can either intervene to
prevent or be held responsible for harmful system outputs (Jotterand & Bosco,
2020; Rahwan, 2018).
Scope, Limitations, andOutline
One governance mechanism that merits further examination is ethics-based audit-
ing (EBA) (Diakopoulos, 2015; Raji etal., 2020; Brown etal., 2021; Mökander &
Floridi, 2021). Operationally, EBA is characterised by a structured process whereby
an entity’s present or past behaviour is assessed for consistency with relevant princi-
ples or norms (Brundage etal., 2020). The main idea underpinning EBA is that the
causal chain behind decisions made by ADMS can be revealed by improved proce-
dural transparency and regularity, which, in turn, allow stakeholders to identify who
should be held accountable for potential ethical harms. Importantly, however,EBA
doesnotattempt to codify ethics.Rather, it helps identify, visualise, and commu-
nicate whichever normative values are embedded in a system. Theaim thereby is
to spark ethical deliberation amongst software developers and managersin organi-
sations that design and deploy ADMS. This implies that while EBA can provide
useful and relevant information, it does not tell human decision-makers how to act
on that information. That said, by strengthening trust between different stakeholders
and promoting transparency, EBA can facilitate morally good actions (more on this
in sectionEthics-based Auditing).
The idea of auditing software is not new. Since the 1970s, computer scientists
have been researching how to ensure that different software systems adhere to pre-
defined functionality and reliability standards (Weiss, 1980). Nor is the idea of
auditing ADMS for consistency with ethics principles new. In 2014, Sandvig etal.
referred to ‘auditing of algorithms’ as a promising, yet underexplored, governance
mechanism to address the ethical challenges posed by ADMS. Since then, EBA has
attracted much attention from policymakers, researchers, and industry practition-
ers alike. For example, regulators like the UK Information Commissioner’s Office
(ICO) have drafted AI auditing frameworks (ICO, 2020; Kazim etal., 2021). At the
same time, traditional accounting firms, including PwC (2019) and Deloitte (2020),
technology-based startups like ORCAA (2020), and all-volunteer organisations like
ForHumanity (2021) are all developing tools to help clients verify claims about
their ADMS. However, despite a growing interest in EBA from both policymak-
ers and private companies, important aspects of EBA are yet to be substantiated by
academic research. In particular, a theoretical foundation for explaining how EBA
affords good governance has hitherto been lacking.
In this article, we attempt to close this knowledge gap by analysing the feasibility
and efficacy of EBA as a governance mechanism that allows organisations to opera-
tionalise their ethical commitments and validate claims made about their ADMS.
Potentially, EBA can also serve the purpose of helping individuals understand how
a specific decision was made as well as how to contest it. Our primary focus, how-
ever, is on the affordances and constraints of EBA as an organisational governance
mechanism. The purpose thereby is to contribute to an improved understanding of
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Ethics-Based Auditing of Automated Decision-Making Systems Page 5 of 30 44
what EBA is and how it can help organisations develop and deploy ethically-sound
ADMS in practice.
To narrow down the scope of our analysis, we introduced two further limitations.
First, we do not address any legal aspects of auditing. Rather, our focus in this arti-
cle is on ethical alignment, i.e. on what ought and ought not to be done over and
above compliance with existing regulation. This is not to say that hard governance
mechanisms (like laws and regulations) are superfluous. In contrast, as stipulated by
the AI HLEG (2019), ADMS should be lawful, ethical, and technically robust. How-
ever, hard and soft governance mechanisms often complement each other, and deci-
sions made by ADMS can be ethically problematic and deserving of scrutiny even
when not illegal (Floridi, 2018). Hence, from now on, ‘EBA’ is to be understood as
a soft yet formal1 ‘post-compliance’ governance mechanism.
Second, any review of normative ethics frameworks remains outside the scope
of this article. When designing and operating ADMS, tensions may arise between
different ethical principles for which there are no fixed solutions (Kleinberg etal.,
2017). For example, a particular ADMS may improve the overall accuracy of deci-
sions but discriminate against specific subgroups in the population (Whittlestone
etal., 2019a). Similarly, different definitions of fairness—like individual fairness,
demographic parity, and equality of opportunity—are mutually exclusive (Friedler
etal., 2016; Kusner et al., 2017). In short, it would be naïve to suppose that we
have to(or indeed even can)resolve disagreements in moral and political philosophy
(see e.g. Binns, 2018) before we start to design and deploy ADMS. To overcome
this challenge, we conceptualise EBA as a governance mechanism that can help
organisations adhere to any predefined set of (coherent and justifiable) ethics prin-
ciples (more on this in sectionConceptual Constraints). EBA can, for example, take
place within one of the ethical frameworks already mentioned, especially the Eth-
ics Guidelines for Trustworthy AI (AI HLEG, 2019) for countries belonging to the
European Union and the Recommendation of the Council on Artificial Intelligence
(OECD, 2019) for countries that officially adopted the OECD principles. But organ-
isations that design and deploy ADMS may also formulate their own sets of ethics
principles and use these as a baseline to audit. The main takeaway here is that EBA
is not morally good in itself, nor it is sufficient to guarantee morally good outcomes.
EBA enables moral goodness to be realised, if properly implemented and combined
with justifiable values and sincere intentions (Floridi, 2017a; Taddeo, 2016).
The remainder of this article proceeds as follows. In sectionAutomated Decision-
Making Systems, we define ‘ADMS’ and discuss the central features of ADMS that
give rise to ethical challenges. In sectionEthics-based Auditing, we explain what
EBA is (or should be) in the context of ADMS. In doing so, we also clarify the roles
and responsibilities of different stakeholders in relation to the EBA procedures. In
sectionStatus Quo: Existing EBA Frameworks and Tools, we provide an overview
of currently available frameworks and tools for EBA of ADMS and how are these
being implemented. We then offer three main contributions to the existing literature.
1 Formal (as opposed to informal) governance mechanisms are officially stated, documented, and com-
municated by the organisation that employs them (Falkenberg & Herremans 1995).
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First, in sectionA Vision for Ethics-based Auditing of ADMS, we articulate how
EBA can support good governance. Second, in section Criteria for Successful
Implementation, we identify seven criteria for how to implement EBA procedures
successfully. Third, in sectionDiscussion: Constraints Associated with Ethics-based
Auditing, we highlight and discuss the constraints associated with EBA of ADMS.
In sectionConclusions, we conclude that EBA, as outlined in this article, can help
organisations manage some of the ethical risks posed by ADMS while allowing
societies to reap the economic and social benefits of automation.
Automated Decision‑Making Systems
While ‘algorithms’, ‘AI’ and ‘ADMS’ are often used interchangeably, we prefer to
use the term ADMS because it captures more precisely the essential features of
the systems under investigation. Throughout this article, we are using the follow-
ing definition of ADMS provided by AlgorithmWatch in their report Automating
Society (2019).
[Automatic Decision-Making System] refers to sociotechnical systems
that encompass a decision-making model, an algorithm that translates this
model into computable code, the data this code uses as an input, and the
entire environment surrounding its use.
From an ethical perspective, it is primarily the autonomous, complex, and scalable
nature of ADMS that either introduces new types of challenges or exacerbates exist-
ing societal tensions and inequalities. Although interlinked and mutually reinforcing,
these three features pose different conceptual challenges. The autonomous nature
of ADMS makes it difficult to predict and assign accountability when harms occur
(Coeckelbergh, 2020; Tutt, 2017). Traditionally, the actions of technical systems
have been linked to the user, the owner, or the manufacturer of the system. How-
ever, the ability of ADMS to adjust their behaviour over time undermines existing
chains of accountability (Dignum, 2017). Moreover, it is increasingly possible that
a network of agents—some human, some non-human—may cause morally loaded
actions (Floridi, 2016a). The appearance of ADMS thereby challenges notions of
moral agents as necessarily human in nature (Floridi, 2013).
Similarly, the complex, often opaque, nature of ADMS may hinder the possibil-
ity of linking the outcome of an action to its causes (Oxborough etal., 2018). For
example, the structures that enable learning in neural networks, including the use of
hidden layers, contributes to technical opacity that may undermine the attribution of
accountability for the action of ADMS (Citron & Pasquale, 2014). While it should
be noted that opacity can also be a result of intentional corporate or state secrecy
(Burrell, 2016), our main concern here relates to inherent technical complexity.
Finally, the scalability of ADMS implies that it will become more difficult to
manage system externalities, as they may be hard to predict and spill across bor-
ders and generations (Dafoe, 2017). This makes it challenging to define and rec-
oncile different legitimate values and interests. The problem posed by the scal-
ability of ADMS is thus not only that norms will become harder to uphold but
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Ethics-Based Auditing of Automated Decision-Making Systems Page 7 of 30 44
also harder to agree upon in the first place. Of course, the levels of autonomy,
complexity, and scalability displayed by ADMS are all matters of degree (Tasiou-
las, 2018). For example, in some cases, ADMS act in full autonomy, whereas in
others, ADMS provide recommendations to a human operator who has the final
say (Cummings, 2004). In terms of complexity, a similar distinction can be made
between ADMS that automate routine tasks and those which learn from their
environment to achieve goals.
From a governance perspective, it is useful to view highly autonomous and
self-learning ADMS as parts of larger sociotechnical systems. Because ADMS
adapt their behaviour based on external inputs and interactions with their envi-
ronments (van de Poel, 2020), important dynamics of the system as a whole
may be lost or misunderstood if technical subsystems are targeted separately (Di
Maio, 2014). This risk is summarised by what Lauer (2020) calls the fallacy of
the broken part: when there is a malfunction, the first instinct is to identify and
fix the broken part. Yet most serious errors or accidents associated with ADMS
can be traced not to coding errors but requirement flaws (Leveson, 2011). This
implies that no purely technical solution will be able to ensure that ADMS are
ethically-sound (Kim, 2017). It also implies that audits need to consider not only
the source code of an ADMS and the purpose for which it is employed, but also
the actual impact it exerts on its environment as well as the normative goals of
relevant stakeholders.
Ethics‑based Auditing
EBA is a governance mechanism that can be used by organisations to control or
influence the ways in which ADMS are designed and deployed, and thereby, indi-
rectly, shape the resultant characteristics of these systems (Mökander & Floridi,
2021). As mentioned in the introduction, EBA is characterised by a structured pro-
cess whereby an entity’s present or past behaviour is assessed for consistency with
relevant principles or norms (Brundage etal., 2020). It is worth noting that the entity
in question, i.e. the subject of the audit, can be a person, an organisational unit, or a
technical system. Taking a holistic approach, we argue that these different types of
EBA are complementary.
Further, we use the expression ’ethics-based’ instead of ’ethical’ to avoid any
confusion: We do neither refer to a kind of auditing conducted ethically, nor to the
ethical use of ADMS in auditing, but to an auditing process that assesses ADMS
based on their adherence to predefined ethics principles. Thus, EBA shifts the focus
of the discussion from the abstract to the operational, and from guiding principles
to managerial intervention throughout the product lifecycle, thereby permeating the
conceptualisation, design, deployment and use of ADMS.
While widely accepted standards for EBA of ADMS have yet to emerge, it is pos-
sible to distinguish between different approaches. For example, functionality audits
focus on the rationale behind decisions; code audits entail reviewing the source code
of an algorithm; and impact audits investigate the types, severity, and prevalence of
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effects of an algorithm’s outputs (Mittelstadt, 2016). Again, these approaches are
complementary and can be combined to design and implement EBA procedures in
ways that are feasible and effective (more on this in sectionConnecting the Dots).
Importantly, EBA differs from merely publishing a code of conduct, since its
central activity consists of demonstrating adherence to a predefined baseline (ICO,
2020). EBA also differs from certification in important aspects. For example, the
process of certification is typically aimed at producing an official document that
attests to a particular status or level of achievement (Scherer, 2016). To this end, cer-
tifications are issued by a third party, whereas auditing can (in theory) alsobe done
by (parts of) an organisation over itself for purely internal purposes. In sum, under-
stood as a process of informing, interlinking, and assessing existing governance
structures, EBA can provide the basis for—but is not reducible to—certification.
As a governance mechanism, ‘auditing’ (as commonly understood) has a long
history of promoting trust and transparency in areas like security and financial
accounting (LaBrie & Steinke, 2019). Valuable lessons can be learned from these
domains. Most importantly, the process of ‘auditing’ is always purpose-oriented. In
our case, EBA is directed towards the goal of ensuring that ADMS operate in ways
that align with specific ethics guidelines. Throughout this purpose-oriented process,
various tools (such as software programs and standardised reporting formats) and
methods (like stakeholder consultation or adversarial testing) are employed to verify
claims and create traceable documentation. This documentation process enables the
identification of the reasons why an ADMS was erroneous, which, in turn, could
help anticipate undesired consequences for certain stakeholders and prevent future
mistakes (Felzmann etal., 2020). Naturally, different EBA procedures employ dif-
ferent tools and contain different steps. The protocols that govern specific EBA pro-
cedures are hereafter referred to as EBA frameworks.
Another lesson is that ‘auditing’ presupposes operational independence between
the auditor and the auditee. Whether the auditor is a government body, a third-party
contractor, an industry association, or a specially designated function within larger
organisations, the main point is to ensure that the audit is run independently from
the regular chain of command within organisations (Power, 1999). The reason for
this is not only to minimise the risk of collusion between auditors and auditees but
Fig. 1 Roles and responsibilities during independent audits
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Ethics-Based Auditing of Automated Decision-Making Systems Page 9 of 30 44
also to clarify roles so as to be able to allocate responsibility for different types of
harm or system failures (IIA, 2017).
Figure1 below illustrates the relationships between organisations that design and
deploy ADMS (who are accountable for the behaviour of their systems), the man-
agement of such organisations (who are responsible for achieving organisational
goals, including adhering to ethical values), the independent auditor (who is tasked
with objectively reviewing and assessing how well an organisation adheres to rel-
evant principles and norms), and regulators (who are monitoring the compliance of
organisations on behalf of the government and decision-making subjects). For EBA
to be effective, auditors must be able to test ADMS for a wide variety of typical and
atypical scenarios. Regulators can therefore support the emergence and implementa-
tion of voluntary EBA procedures by providing the necessary infrastructure to share
information and create standardised reporting formats and evaluation criteria (Keyes
etal., 2019).
Status Quo: Existing EBA Frameworks andTools
In this section, we survey the landscape of currently available EBA frameworks and
tools. In doing so, we illustrate how EBA can provide new ways of detecting, under-
standing, and mitigating the unwanted consequences of ADMS.
Ethics‑based Auditing Frameworks
As described in the previous section, EBA frameworks are protocols that describe a
specific EBA procedure and define what is to be audited, by whom, and according to
which standards. Typically, EBA frameworks originate from one of two processes.
The first type consists of ‘top-down’ national or regional strategies, like those pub-
lished by the Government of Australia (Dawson etal., 2019) or Smart Dubai (2019).
These strategies tend to focus on legal aspects or stipulate normative guidelines.2
At a European level, the debate was shaped by the AI4People project, which
proposed that ‘auditing mechanisms’ should be developed to identify unwanted
ethical consequences of ADMS (Floridi etal., 2018). Since then, the AI HLEG3
has published not only the Ethics-Guidelines for Trustworthy AI (2019), but also
a corresponding Assessment List for Trustworthy AI (2020). This assessment list is
intended for self-evaluation purposes and can thus be incorporated into EBA proce-
dures. Such checklists are simple tools that help designers get a more informed view
of edge cases and system failures (Raji etal., 2020). Most recently, the European
Commission (2021) published its long-anticipated proposal of the new EU Artifi-
cial Intelligence Act. The proposed regulation takes a risk-based approach. For our
2 For a review of existing sets of ethics principles and national or regional AI governance frameworks,
interested readers are directed to either The Ethics of AI Ethics(Hagendorff, 2020) or (Floridi & Cowls,
3 The AI HLEG is an independent expert group set up by the European Commission in June 2018.
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purposes, this means that a specific ADMS can be classified into one of four risk
levels. While ADMS that pose ‘unacceptable risk’ are proposed to be completely
banned, so-called ‘high-risk’ systems will be required to undergo legally mandated
ex-ante and ex-post conformity assessments. However, even for ADMS that pose
‘minimal’ or ‘limited’ risk, the European Commission encourages organisations that
design and deploy such systems to adhere to voluntary codes of conduct. In short,
with respect to the proposed European regulation, there is a scope for EBA to help
both providers of ADMS that pose limited risk to meet basic transparency obliga-
tions and providers of high-risk systems to demonstrate adherence to organisational
values that goes over and above what is legally required.
The second type of EBA frameworks emerges ‘bottom-up’, from the expansion of
data regulation authorities to account for the effects ADMS have on informational
privacy. Building on an extensive experience of translating ethical principles into
governance protocols, frameworks developed by data regulation agencies provide
valuable blueprints for EBA of ADMS. The CNIL privacy impact assessment, for
example, requires organisations to describe the context of the data processing under
consideration when analysing how well procedures align with fundamental ethical
principles (CNIL, 2019). This need for contextualisation applies not only to data
management but also to the use of ADMS at large. Another transferable lesson is
that organisations should conduct an independent ethical evaluation of software they
procure from—or outsource production to—third-party vendors (ICO, 2018). At the
same time, EBA frameworks with roots in data regulation tend to account only for
specific ethical concerns, e.g. those related to privacy. This calls for caution. Since
there is a plurality of ethical values which may serve as legitimate normative ends
(think of freedom, equality, justice, proportionality, etc.), an exclusive focus on one,
or even a few, ethical challenges risks leading to sub-optimisation from a holistic
To synthesise, the reviewed EBA frameworks converge around a procedure based
on impact assessments. IAF (2019) summarised this procedure in eight steps: (1)
Describe the purpose of the ADMS; (2) Define the standards or verifiable criteria
based on which the ADMS should be assessed; (3) Disclose the process, including
a full account of the data use and parties involved; (4) Assess the impact the ADMS
has on individuals, communities, and its environment; (5) Evaluate whether the ben-
efits and mitigated risks justify the use of ADMS; (6) Determine the extent to which
the system is reliable, safe, and transparent; (7) Document the results and considera-
tions; and (8) Reflect and evaluate periodically, i.e. create a feedback loop.
Ethics‑based Auditing Tools
EBA tools are conceptual models or software products that help measure, evaluate,
or visualise one or more properties of ADMS. With the aim to enable and facilitate
EBA of ADMS, a great variety of such tools have already been developed by both
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Ethics-Based Auditing of Automated Decision-Making Systems Page 11 of 30 44
academic researchers and privately employed data scientists. While these tools typi-
cally apply mathematical definitions of principles like fairness, accountability and
transparency to measure and evaluate the ethical alignment of ADMS (Keyes etal.,
2019), different tools help ensure the ethical alignment of ADMS in different ways.
A full review of all the tools that organisations can employ during EBA procedures
would be beyond the scope of this article. Nevertheless, in what follows, we provide
some examples of different types of tools that help organisations design and develop
ethically-sound ADMS.4
Some tools facilitate the audit process by visualising the outputs of ADMS.
FAIRVIS, for example, is a visual analytics system that integrates a subgroup dis-
covery technique, thereby informing normative discussions about group fairness
(Cabrera etal., 2019). Another example is Fairlearn, an open-source toolkit that
treats any ADMS as a black box. Fairlearn’s interactive visualisation dashboard
helps users compare the performance of different models (Microsoft, 2020).
These tools are based on the idea that visualisation helps developers and auditors
to create more equitable algorithmic systems.
Other tools improve the interpretability of complex ADMS by generating more
straightforward rules that explain their predictions. For example, Shapley Addi-
tive exPlanations, or SHAP, calculates the marginal contribution of relevant fea-
tures underlying a model’s prediction (Leslie, 2019). The explanations provided
by such tools are useful, e.g. when determining whether protected features have
unjustifiably contributed to a decision made by ADMS. However, such explana-
tions also have important limitations. For example, tools that explain the contri-
bution of features that have been intentionally used as decision inputs may not
determine whether protected features have contributed unjustifiably to a decision
through proxy variables.
Yet other tools help convey the reasoning behind ADMS by applying one of three
strategies: Data-based explanations provide evidence of a model by using compari-
sons with other examples to justify decisions; Model-based explanations focus on
the algorithmic basis of the system itself; and Purpose-based explanations focus
on comparing the stated purpose of a system with the measured outcomes (Kroll,
2018). For our purposes, the key takeaway is that, while different types of expla-
nations are possible, EBA should focus on local interpretability, i.e. explanations
targeted at individual stakeholders—such as decision subjects or external auditors—
and for specific purposes like internal governance, external reputation management,
or third-party verification. Here, a parallel can be made to what Loi etal. (2020)
call transparency as design publicity, whereby organisations that design or deploy
ADMS are expected to publicise the intentional explanation of the use of a specific
system as well as the procedural justification of the decision it takes.
Tools have also been developed that help to democratise the study of ADMS.
Consider the TuringBox, which was developed as part of a time-limited research
project at MIT. This platform allowed software developers to upload the source code
of an ADMS so as to let others examine them (Epstein etal., 2018). The Turing-
Box thereby provided an opportunity for developers to benchmark their system’s
4 The EBA frameworks and tools reviewed in this section are summarised in Appendix A.
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performance with regards to different properties. Simultaneously, the platform also
allowed independent researchers to evaluate the outputs from ADMS, thereby add-
ing an extra layer of procedural transparency to the software development process.
Finally, some tools help organisations document the software development pro-
cess and monitor ADMS throughout their lifecycle. AI Fairness 360 developed by
IBM, for example, includes metrics and algorithms to monitor, detect, and mitigate
bias in datasets and models (Bellamy etal., 2019). Other tools have been developed
to aid developers in making pro-ethical design choices (Floridi, 2016b) by provid-
ing useful information about the properties and limitations of ADMS. Such tools
include end-user license agreements (Responsible AI Licenses, 2021), tools for
detecting bias in datasets (Saleiro etal., 2018), and tools for improving transparency
like datasheets for datasets (Gebru etal., 2018).
A Vision forEthics‑based Auditing ofADMS
Connecting theDots
As demonstrated in sectionStatus Quo: Existing EBA Frameworks and Tools above,
a wide variety of EBA frameworks and tools have already been developed to help
organisations and societies manage the ethical risks posed by ADMS. However,
these tools are often employed in isolation. Hence, to be feasible and effective, EBA
procedures need to combine existing conceptual frameworks and software tools into
a structured process that monitors each stage of the software development lifecycle
to identify and correct the points at which ethical failures (may) occur. In practice,
this means that EBA procedures should combine elements of (a) functionality audit-
ing, which focuses on the rationale behind decisions (and why they are made in the
first place); (b) code auditing, which entails reviewing the source code of an algo-
rithm; and (c) impact auditing, whereby the severity and prevalence of the effects of
an algorithm’s outputs are investigated.
It should be reemphasised that the primary responsibility for identifying and exe-
cuting steps to ensure that ADMS are ethically sound rests with the management of
the organisations that design and operate such systems. In contrast, the independent
auditor’s responsibility is to (i) assess and verify claims made by the auditee about
its processes and ADMS and (ii) ensure that there is sufficient documentation to
respond to potential inquiries from public authorities or individual decision subjects.
More proactively, the process of EBA should also help spark and inform ethical
deliberation throughout the software development process. The idea is that continu-
ous monitoring and assessment ensures that a constant flow of feedback concerning
the ethical behaviour of ADMS is worked into the next iteration of their design and
application. Figure2 below illustrates how the process of EBA runs in parallel with
the software development lifecycle.
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Ethics-Based Auditing of Automated Decision-Making Systems Page 13 of 30 44
Methodological Advantages
EBA of ADMS—as outlined in this article—displays six, interrelated and mutually
reinforcing, methodological advantages. These are best illustrated by examples from
existing tools:
(1) EBA can provide decision-making support to executives and legislators by defin-
ing and monitoring outcomes, e.g. by showing the normative values embedded
in a system (AIEIG, 2020). Here, EBA serves a diagnostic function: before ask-
ing whether we would expect an ADMS to be ethical, we must consider which
mechanisms we have to determine what it is doing at all. By gathering data on
system states (both organisational and technical) and reporting on the same,
EBA enables stakeholders to evaluate the reliability of ADMS in more detail.
A systematic audit is thereby the first step to make informed model selection
decisions and to understand the causes of adverse effects (Saleiro etal., 2018).
(2) EBA can increase public trust in technology and improve user satisfaction by
enhancing operational consistency and procedural transparency. Mechanisms
such as documentation and actionable explanations are essential to help indi-
viduals understand why a decision was reached and contest undesired outcomes
(Wachter etal., 2017). This also has economic implications. While there may be
many justifiable reasons to abstain from using available technologies in certain
contexts, fear and ignorance may lead societies to underuse available technolo-
gies even in cases where they would do more good than harm (Cowls & Floridi,
2018). In such cases, increased public trust in ADMS could help unlock eco-
nomic growth. However, to drive trust in ADMS, explanations need to be action-
able and selective (Barredo Arrieta etal., 2020). This is possible even when
Fig. 2 EBA helps inform, formalise, and interlink existing governance structures through an iterative
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J. Mökander etal.
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algorithms are technically opaque since ADMS can be understood intentionally
and in terms of their inputs and outputs.
(3) EBA allows for local alignment of ethics and legislation. While some normative
metrics must be assumed when evaluating ADMS, EBA is a governance mecha-
nism that allows organisations to choose which set of ethics principles they seek
to adhere to. This allows for contextualisation. Returning to our example with
fairness above, the most important aspect from an EBA perspective is not which
specific definition of fairness is applied in a particular case, but that this deci-
sion is communicated transparently and publicly justified. In short, by focusing
on identifying errors, tensions, and risks, as well as communicating the same to
relevant stakeholders, such as customers or independent industry associations,
EBA can help organisations demonstrate adherence to both sector-specific and
geographically dependent norms and legislation.
(4) EBA can help relieve human suffering by anticipating potential negative conse-
quences before they occur (Raji & Buolamwini, 2019). There are three overarch-
ing strategies to mitigate harm: pre-processing, i.e. reweighing or modifying
input data; in-processing, i.e. model selection or output constraints; and post-
processing, i.e. calibrated odds or adjustment of classifications (Koshiyama,
2019). These strategies are not mutually exclusive. By combining minimum
requirements on system performance with automated controls, EBA can help
both developers test and improve the performance of ADMS (Mahajan etal.,
2020) and enable organisations to establish safeguards against unexpected or
unwanted behaviours.
(5) EBA can help balance conflicts of interest. A right to explanation must, for
example, be reconciled with jurisprudence and counterbalanced with intellectual
property law as well as freedom of expression (Wachter etal., 2017). By con-
taining access to sensitive parts of the review process to authorised third-party
auditors, EBA can provide a basis for accountability while preserving privacy
and intellectual property rights.
(6) EBA can help human decision-makers to allocate accountability by tapping
into existing internal and external governance structures (Bartosch etal., 2018).
Within organisations, EBA can forge links between non-technical executives
and developers. Externally, EBA help organisations validate the functionality
of ADMS. In short, EBA can clarify the roles and responsibilities of different
stakeholders and, by leveraging the capacity of institutions like national civil
courts, help to redress the harms inflicted by ADMS.
Naturally, the methodological advantages highlighted in this section are poten-
tial and far from being guaranteed. However, the extent to which these benefits can
be harnessed in practice depends not only on complex contextual factors but also
on how EBA frameworks are designed. To realise its full potential as a governance
mechanism, EBA of ADMS needs to meet specific criteria. In the next section, we
turn to specifying these criteria.
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Ethics-Based Auditing of Automated Decision-Making Systems Page 15 of 30 44
Criteria forSuccessful Implementation
Best practices for EBA of ADMS have yet to emerge. Nevertheless, as discussed
in section Status Quo: Existing EBA Frameworks and Tools, organisations and
researchers have already developed, and attempted to pilot, a wide range of EBA
tools and frameworks. These early attempts hold valuable and generalisable lessons
for organisations that wish to implement feasible and effective EBA procedures. As
we will see, some of these lessons concern how stakeholders view EBA of ADMS,
whilst other lessons concern the design of EBA practices. In this section, we will
discuss the most important lessons from previous work and condense these into cri-
teria for how to get EBA of ADMS right.
As a starting point, it should be acknowledged that ADMS are not isolated tech-
nologies. Rather, ADMS are both shaped by and help shape larger sociotechnical
systems (Dignum, 2017). Hence, system output cannot be considered biased or erro-
neous without some knowledge of the available alternatives. Holistic approaches
to EBA of ADMS must therefore seek input from diverse stakeholders, e.g. for an
inclusive discourse about key performance indicators (KPI). However, regardless of
which KPI an organisation chooses to adopt, audits are only meaningful insofar as
they allow organisations to verify claims made about their ADMS. This implies that
EBA procedures themselves must be traceable. By providing a traceable log of the
steps taken in the design and development of ADMS, audit trails can help organisa-
tions verify claims about their engineered systems (Brundage etal., 2020). Here, a
distinction should be made between traceability and transparency: while transpar-
ency is often invoked to improve trust (Springer & Whittaker, 2019), full transpar-
ency concerning the content of audits may not be desirable (e.g. with regards to
privacy- and intellectual property rights). Instead, what counts is procedural trans-
parency and regularity.
Further, to ensure that ADMS are ethically-sound, organisational policies need
to be broken down into tasks for which individual agents can be held accountable
(Ananny & Crawford, 2018). By formalising the software development process
and revealing (parts of) the causal chain behind decisions made by ADMS, EBA
helps clarify the roles and responsibilities of different stakeholders, including execu-
tives, process owners, and data scientists. However, allocating responsibilities is not
enough. Sustaining a culture of trust also requires that people who breach ethical
and social norms are subject to proportional sanctions (Ellemers etal., 2019). By
providing avenues for whistle-blowers and promoting a culture of ethical behaviour,
EBA also helps strengthen interpersonal accountability within organisations (Koene
etal., 2019). At the same time, doing the right thing should be made easy. This can
be achieved through strategic governance structures that align profit with purpose.
The ‘trustworthiness’ of a specific ADMS is never just a question about technology
but also about value alignment (Christian, 2020; Gabriel, 2020). In practice, this
means that the checks and balances developed to ensure safe and benevolent ADMS
must be incorporated into organisational strategies, policies, and reward structures.
Importantly, EBA does not provide an answer sheet but a playbook. This means
that EBA of ADMS should be viewed as a dialectic process wherein the auditor
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ensures that the right questions are asked (Goodman, 2016) and answered ade-
quately. This means that auditors and systems owners should work together to
develop context-specific methods (Schulam & Saria, 2019). To manage the risk that
independent auditors would be too easy on their clients, licences should be revoked
from both auditors and system owners in cases where ADMS fail. However, it is dif-
ficult to ensure that an ADMS contains no bias or to guarantee its fairness (Micro-
soft, 2020). Instead, the goal from an EBA perspective should be to provide useful
information about when an ADMS is causing harm or when it is behaving in a way
that is different from what is expected. This pragmatic insight implies that audits
need to monitor and evaluate system outputs continuously, i.e. through ‘oversight
programs’ (Etzioni & Etzioni, 2016), and document performance characteristics in
a comprehensible way (Mitchell etal., 2019). Hence, continuous EBA of ADMS
implies considering system impacts as well as organisations, people, processes, and
Finally, the alignment between ADMS and specific ethical values is a design
question. Ideally, properties like interpretability and robustness should be built into
systems from the start, e.g. through ‘Value-Aligned Design’ (Bryson & Winfield,
2017). However, the context-dependent behaviour of ADMS makes it difficult to
anticipate the impact ADMS will have on the complex environments in which they
operate (Chopra & Singh, 2018). By incorporating an active feedback element into
the software development process, EBA can help inform the continuous re-design
of ADMS. Although this may seem radical, it is already happening: most sciences,
including engineering and jurisprudence, do not only study their systems, they
simultaneously build and modify them (Floridi, 2017b).
Taken together, these generalisable lessons suggest that EBA procedures, even
imperfectly implemented, can make a real difference to the ways in which ADMS
are designed and deployed. However, our analysis of previous work also finds that,
in order to be feasible and effective, EBA procedures must meet seven criteria. More
specifically, to help organisations manage the ethical risks posed by ADMS, we
argue that EBA procedures should be:
(1) Holistic, i.e. treat ADMS as an integrated component of larger sociotechnical
(2) Traceable, i.e. assign responsibilities and document decisions to enable
(3) Accountable, i.e. help link unethical behaviours to proportional sanctions
(4) Strategic, i.e. align ethical values with policies, organisational strategies,
and incentives
(5) Dialectic, i.e. view EBA as a constructive and collaborative process
(6) Continuous, i.e. identify, monitor, evaluate, and communicate system
impacts over time
(7) Driving re-design, i.e. provide feedback and inform the continuous re-design
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Ethics-Based Auditing of Automated Decision-Making Systems Page 17 of 30 44
Of course, these criteria are aspirational and, in practice, unlikely to be satisfied
all at once. Nevertheless, we must not let perfect be the enemy of good. Policymak-
ers and organisations that design and deploy ADMS are thus advised to consider
these seven criteria when developing and implementing EBA procedures.
Discussion: Constraints Associated withEthics‑based Auditing
Despite the methodological advantages identified in sectionA Vision for Ethics-
based Auditing of ADMS, it is important to remain realistic about what EBA can,
and cannot, be expected to achieve. Our analysis of existing tools and frameworks
suggests that EBA of ADMS—even if implemented according to the criteria listed
in sectionCriteria for Successful Implementation—is subject to a range of concep-
tual, technical, social, economic, organisational, and institutional constraints. For an
overview, please find these constraints summarised in table format in Appendix C.
In the remainder of this section, we highlight and discuss the most pressing con-
straints associated with EBA of ADMS. To design feasible and effective EBA proce-
dures, these constraints must be understood and accounted for.
Conceptual Constraints
Conceptual constraints cannot be easily overcome by means of technical innovation
or political decision. Instead, they must be managed continuously by balancing the
need for ethical alignment with tolerance and respect for pluralism. Insofar as ethical
guidelines often mask unresolved disputes about the definitions of normative con-
cepts like fairness and justice, EBA of ADMS may be conceptually constrained by
hidden political tensions. For example, the reviewed literature accommodates more
than six definitions of fairness, including individual fairness, demographic parity,
and equality of opportunity (Kusner etal., 2017). Some of these interpretations are
mutually exclusive, and specific definitions of fairness can even increase discrimina-
tion according to others.
While EBA of ADMS can help ensure compliance with a given policy, how to
prioritise between conflicting interpretations of ethical principles remains a norma-
tive question. This is because translating principles into practice often requires trade-
offs between different legitimate, yet conflicting normative values. Using personal
data, for example, may improve public services by tailoring them but compromise
privacy. Similarly, while increased automation could make lives more convenient,
it also risks undermining human autonomy. How to negotiate justifiable trade-offs
is a context-dependent, multi-variable problem. While audits cannot guarantee that
a justifiable balance has been struck, the identification, evaluation, and communica-
tion of trade-offs can be included as assessment criteria. One function of EBA is
thus to make visible implicit choices and tensions, give voice to different stakehold-
ers, and arrive at resolutions that, even when imperfect, are at least publicly defensi-
ble (Whittlestone etal., 2019b).
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Moreover, EBA is constrained by the difficulty of quantifying externalities that
occur due to indirect causal chains over time. This problem is exacerbated by the
fact that the quantification of social phenomena inevitably strips away local knowl-
edge and context (Mau & Howe, 2019). On the one hand, tools claiming to opera-
tionalise ethics mathematically fall into the trap of technological solutionism (Lipton
& Steinhardt, 2019). On the other hand, tools that focus on only minimum require-
ments provide little incentives for organisations to go beyond the minimum.
Technical Constraints
Technical constraints are tied to the autonomous, complex, and scalable nature of
ADMS. These constraints are time and context-dependent and thus likely to be
relieved or transformed by future research. Three of them are worth highlighting.
First, consider how the complexity and the lack of transparency of machine learning
models hinder their interpretation (Oxborough etal., 2018). Such characteristics of
ADMS limit the effectiveness of audits insofar as they make it difficult to assign and
trace responsibility when harm occurs. Technical complexity also makes it difficult
to audit a system without perturbing it. Further, there is a risk that sensitive data
may be exposed during the audit process itself (Kolhar etal., 2017). To manage this
challenge, third party auditors can be given privileged and secured access to private
information to assess whether claims about the safety, privacy, and accuracy made
by the system developer are valid. As of today, however, most EBA schemes do not
protect user data from third-party auditors.
A second technical constraint stems from the use of agile software develop-
ment methods. The same agile qualities that help developers meet rapidly chang-
ing customer requirements also make it difficult for them to ensure compliance with
pre-specified requirements. One approach to managing this tension is to incorpo-
rate agile methodologies (see e.g. Strenge & Schack, 2020) that make use of ‘liv-
ing traceability’ in the audit process. These methods provide snapshots of programs
under development in real-time (Steghöfer etal., 2019). Despite the availability of
such pragmatic fixes, however, the effectiveness of EBA remains limited by an ina-
bility to ensure the compliance of systems that are yet to emerge.
Finally, EBA is technically constrained by the fact that laboratories differ from
real-life environments (Auer & Felderer, 2018). Put differently, given the data- and
context-dependent behaviour of ADMS, only limited reasoning about their later per-
formance is possible based on testing in controlled settings. To manage this chal-
lenge, test environments for simulation can be complemented by continuous EBA
of live applications which constantly execute the algorithm. One example is ‘live
experimentation’, i.e. the controlled deployment of experimental features in live
systems to collect runtime data and analyse the corresponding effect (Fagerholm
etal., 2014). Still, meaningful quality assurance is not always possible within test
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Economic andSocial Constraints
Economic and social constraints are those derived from the incentives of different
actors. Unless these incentives are aligned with the normative vision for ethically-
sound ADMS, economic and social constraints will reduce both the feasibility and
effectiveness of EBA. Inevitably, EBA imposes costs, financial and otherwise. Even
when the costs of audits are justifiable compared to the aggregated benefits, soci-
ety will face questions about which stakeholders would reap which benefits and pay
which costs. For example, the cost of EBA risks having a disproportionate impact
on smaller companies (Goodman, 2016). Similarly, licensing systems for ADMS
are likely to be selectively imposed on specific sectors, like healthcare or air traffic
(Council of Europe, 2018). The point is that both the costs and benefits associated
with EBA should be distributed to not unduly burden or benefit particular groups
in, or sectors of, society. Similarly, demands for ethical alignment must be balanced
with incentives for innovation and adoption. Pursuing rapid technological progress
leaves little time to ensure that developments are robust and ethical (Whittlestone
etal., 2019b). Thus, companies find themselves wedged between the benefits of dis-
ruptive innovation and social responsibility (Turner Lee, 2018) and may not act ethi-
cally in the absence of oversight.
Moreover, there is always a risk of adversarial behaviour during audits. The
ADMS being audited may, for example, attempt to trick the auditor (Rahwan, 2018).
An example of such behaviour was the diesel emission scandal, during which Volk-
swagen intentionally bypassed regulations by installing software that manipulated
exhaust gases during tests (Conrad, 2018). An associated risk is that emerging EBA
frameworks end up reflecting and reinforcing existing power relations. Given an
asymmetry in both know-how and computational resources between data controllers
and public authorities, auditors may struggle to review ADMS (Kroll, 2018). For
example, industry representatives may choose not to reveal insider knowledge but
instead use their informational advantage to obtain weaker standards (Koene etal.,
2019). Sector-specific approaches may therefore lead to a shift of power and respon-
sibility from juridical courts to private actors. Even if, in such a scenario, audits
reveal flaws within ADMS, asymmetries of power may prevent corrective steps from
being taken.
Another concern relates to the fact that ADMS increasingly mediate human
interactions. From an EBA perspective, nudging, i.e. the process of influencing per-
sonal preferences through positive reinforcement or indirect suggestion (Thaler &
Sunstein, 2008), may shift the normative baseline against which ethical alignment
is benchmarked. This risk is aggravated by ‘automation bias’, i.e. the tendency
amongst humans to trust information that originates from machines more than
their own judgement (Cummings, 2004). Consequently, the potentially transforma-
tive effects associated with ADMS pose challenges for how to trigger and evaluate
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Organisational andInstitutional Constraints
Organisational and institutional constraints concern the operational design of EBA
frameworks. Because these constraints depend on legal sanctioning, they are inevi-
tably linked to questions about power. Who audits whom? As of today, a clear insti-
tutional structure is lacking. To establish integrity and validity, EBA of ADMS must
therefore adhere to a transparent and well-recognised process. However, both inter-
nal audits and those performed by professional service providers are subject to con-
cerns about objectivity. A more plausible way to mandate EBA of ADMS would be
the creation of a regulatory body to oversee system owners and auditors. Just as the
Food and Drug Administration tests and approves medicines, a similar agency could
be set up to approve specific types of ADMS (Tutt, 2017). Such an agency would be
able to engage in ex ante regulation rather than relying on ex post judicial enforce-
ment. However, the main takeaway is that EBA will only be as good as the institu-
tion backing it (Boddington etal., 2017).
In a similar vein, EBA is only effective if auditors have access to the information
and resources required to carry out rigorous and meaningful audits. Thus, EBA is
infeasible without strong regulatory compulsion or cooperation from system own-
ers. Data controllers have an interest not to disclose trade secrets. Moreover, the
resources required to audit ADMS can easily exceed those available to auditors. If,
for example, auditors have no information about special category membership, they
cannot determine whether a disparate impact exists. Consequently, the effectiveness
of EBA is constrained by a lack of access to both relevant information and resources
in terms of manpower and computing power.
There are also fundamental tensions between national jurisdictions and the global
nature of technologies (Erdelyi & Goldsmith, 2018). Thus, rules need to be harmo-
nised across domains and boarders. However, such efforts face a hard dilemma. On
the one hand, the lack of shared ethical standards for ADMS may lead to protection-
ism and nationalism. On the other hand, policy discrepancies may cause a race to
the bottom where organisations seek to establish themselves in territories that pro-
vide a minimal tax burden and maximum freedom for technological experimentation
(Floridi, 2019). As a result, the effectiveness of EBA of ADMS remains constrained
by the lack of international coordination.
The responsibility to ensure that ADMS are ethically-sound lies with the organisa-
tions that develop and operate them. EBA—as outlined in this article—is a govern-
ance mechanism that helps organisations not only to ensure but also demonstrate
that their ADMS adhere to specific ethics principles. Of course, this does not mean
that traditional governance mechanisms are redundant. On the contrary, by contrib-
uting to procedural regularity and transparency, EBA of ADMS is meant to comple-
ment, enhance, and interlink other governance mechanisms like human oversight,
certification, and regulation. For example, by demanding that ethics principles and
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Ethics-Based Auditing of Automated Decision-Making Systems Page 21 of 30 44
codes of conduct are clearly stated and publicly communicated, EBA ensures that
organisational practices are subject to additional scrutiny which, in turn, may coun-
teract ‘ethics shopping’. Similarly, EBA helps reduce the risk for ‘ethics bluewash-
ing’ by allowing organisations to validate the claims made about their ethical con-
duct and the ADMS they operate. Thereby, EBA constitutes an integral component
of multifaceted approaches to managing the ethical risks posed by ADMS.
In particular, continuous EBA can help address some of the ethical challenges
posed by autonomous, complex, and scalable ADMS. However, even in contexts
where EBA is necessary to ensure ethical alignment of ADMS, it is by no means
sufficient. For example, it remains unfeasible to anticipate all long-term and indirect
consequences of a particular decision made by an ADMS. Further, while EBA can
help ensure alignment with a given policy, how to prioritise between irreconcilable
normative values remains a fundamentally normative question. Thus, even if private
initiatives to develop EBA mechanisms should be encouraged, the shift of power
and ultimate responsibility from juridical courts to private actorsmust be resisted.
The solution here is that regulators should retain supreme sanctioning power by
authorising independent agencies which, in turn, conduct EBA.
The constraints highlighted in this article do not seek to diminish the merits of
EBA of ADMS. In contrast, our aim has been to provide a roadmap for future work.
While all constraints listed constitute important fields of research, social concerns
related to the potentially transformative effects of ADMS deserve specific attention.
By shifting the normative base on which liberal democracy is built, ADMS may
undermine this trust. Therefore, the design and implementation of EBA frameworks
must be viewed as a part of—and not separated from—the debate about the type of
society humanity wants to live in, and what moral compromises individuals are will-
ing to strike in its making.
In conclusion, standardised EBA procedures can help organisations validate
claims about their ADMS and help strengthen the institutional trust that is founda-
tional for good governance. However, EBA will not and should not replace the need
for continuous ethical reflection and deliberation among individual moral agents.
Appendix A: List ofReviewed EBA Frameworks andTools
Table 1 below summarised the EBA tools and frameworks reviewed in sectionEth-
ics-based Auditing Frameworks. The Table thereby (non-exhaustively) lists some of
the most recent and important contributions to developing EBA of ADMS (for the
methodology used to produce this Table, see Appendix B).
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Appendix B: Methodology
As mentioned in the introduction, the purpose of this article was to contribute to an
improved understanding of what EBA is and how it can help organisations develop
and deploy ethically-sound ADMS in practice. To achieve this aim, we let the fol-
lowing three questions guide the research that led up to this article:
(1) What EBA tools and frameworks are currently available to ensure ethical align-
ment of ADMS, and how are these being implemented?
(2) How can EBA of ADMS help organisations and society reap the full benefit of
new technologies while mitigating the ethical risks associated with ADMS?
(3) What are the conceptual, technical, economic, social, organisational, and insti-
tutional constraints associated with auditing of ADMS?
Questions (1)–(3) are listed in logical order, but chronologically (2) takes pri-
ority, so we began with a systematised review of existing literature to address (2).
The collection phase involved searching five databases (Google Scholar, Scopus,
SSRN, Web of Science and arXiv) for articles related to auditing of ADMS. Key-
words for the search included (‘auditing, ‘evaluation’, OR ‘assessment’) AND (‘eth-
ics’, ‘fairness’, transparency’, OR ‘robust) AND (‘automated decision-making’,
‘artificial intelligence’, OR ‘algorithms’). To limit the scope of the literature review,
we focused on articles published after 2011, the year when IBM Watson marked
the coming of the second wave of AI by beating the two best-ever humans to have
Table 1 List of reviewed EBA frameworks (F) and tools (T)
Institution Publication Type Source
AI ethics impact group Framework to operationalise AI F AIEIG (2020)
CNIL (France) Privacy impact assessment F CNIL (2019)
ECP (Netherlands) AI impact assessment F ECP (2018)
European Commission Guidelines for trustworthy AI F AI HLEG (2019)
Gov. of Australia AI: Australia’s ethics framework F Dawson etal. (2019)
Gov. of Canada Algorithmic impact assessment F Gov. of Canada (2019)
ICO (UK) AI auditing framework (Guidance) F ICO (2020)
PDPC (Singapore) Model AI governance framework F PDPC (2020)
Smart Dubai (UAE) AI ethics principles & guidelines F Smart Dubai (2019)
WEF Facial recognition assessment F WEF (2020)
CMU FAIRVIS T Cabrera etal. (2019)
Google What-if-tool T Google (2020)
IBM AI Fairness 360 T Bellamy etal. (2019)
Microsoft Fairlearn T Microsoft (2020)
MIT Turingbox T Epstein etal. (2018)
PwC Responsible AI toolkit T PwC (2019)
University of Chicago Aequitas T Saleiro etal. (2018)
University of Texas CERTIFAI T Sharma etal. (2019)
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Ethics-Based Auditing of Automated Decision-Making Systems Page 23 of 30 44
competed in the TV quiz show Jeopardy (Susskind & Susskind, 2015). In total, 122
articles and reports were included in the systematised literature review.
In a second step, existing auditing tools and frameworks were reviewed and evaluated
to extract generalisable lessons to address (1) and then (3) by identifying the opportuni-
ties and constraints associated with implementing auditing of ADMS in practice. The
tools and frameworks reviewed for this article, see Table1 in Appendix A, were selected
on the virtue of being recent, relevant, and developed by reputable organisations.
Appendix C: Summary Table ofConstraints Associated withEBA
As emphasises in section Discussion: Constraints Associated with Ethics-based
Auditing, EBA of ADMS is subject to a range of conceptual, technical, social, eco-
nomic, organisational, and institutional constraints. These are summarised in Table
25 below. To design feasible and effective auditing procedures, these constraints
must be understood and accounted for. Our hope is therefore that the constraints
listed below will provide a roadmap for future research, and guide policymakers
attempts to support emerging EBA practices.
Table 2 Summary of constraints associated with EBA of ADMS
Type Constraints
Conceptual Lack of consensus around high-level ethical principles
Normative values conflict and require trade-offs
It is difficult to quantify externalities of complex systems
Information is infallibly lost through reductionist explanations
Technical Complex systems appear opaque and are hard to interpret
Data integrity and privacy are exposed to risks during audits
Linear compliance mechanisms are incompatible with agile develop-
Tests may not be indicative of ADMS behaviour in real-world envi-
Economic and social Auditing may disproportionately disadvantage specific sectors or
Ensuring ethical alignment must be balanced with incentives for
Audits are vulnerable to adversarial behaviour
The transformative effects of ADMS challenge notions of human
Emerging audit frameworks reflect and reinforce existing power rela-
Organisational and institutional There is a lack of institutional clarity about who audits whom
Auditors may lack the access or information required to evaluate
The global nature of ADMS challenge national jurisdictions
5 This summary table was first published in our short commentary article (Mökander & Floridi, 2021).
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J. Mökander etal.
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44 Page 24 of 30
Acknowledgements We wish to thank Maria Axente, Responsible AI Lead at PwC, for her constructive
Author Contributions Jakob Mökander is the main author of the article. Jessica Morley, Mariarosaria
Taddeo and Luciano Floridi contributed equally to the article.
Funding Jakob Mökander was supported by the The Society of Swedish Engineers in Great Britain. Jes-
sica Morley was supported by the Wellcome Trust.
Data Availability Not applicable.
Declaration We hereby declare that this article is our original work and has not been submitted to any
other journal for publication. Further, we have acknowledged all sources used and cited these in the refer-
ence section.
Conflict of interest No conflicts of interest to report.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
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directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen
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Authors and Aliations
JakobMökander1 · JessicaMorley1 · MariarosariaTaddeo1,2 ·
Jessica Morley
Mariarosaria Taddeo
Luciano Floridi
1 Oxford Internet Institute, University ofOxford, 1 St Giles’, OxfordOX13JS, UK
2 Alan Turing Institute, British Library, 96 Euston Rd, LondonNW12DB, UK
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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... One class of technologies that poses particularly challenging ethical questions is algorithmic decisionmaking systems (ADMS) (Lo Piano, 2020;Mittelstadt et al., 2016;Neyland, 2019). ADMS are autonomous self-learning systems that make judgments with little or no direct human intervention (Mökander et al., 2021). They are increasingly utilized by governments, public agencies, and private corporations, making their way into our cities, workplaces, and homes. ...
... While 7 8 these characteristics have been previously identified in the literature for their ethical significance (e.g., Bigman & Gray, 2020;Mökander et al., 2021;Wachter, 2019), our multi-lens ethical approach can inform a nuanced examination of each characteristic to help researchers address them in a comprehensive manner (see Table 3). ...
... Complexity: A third critical characteristic of ADMS is their essential complexity, flowing from the dynamic structures of neural networks and other algorithmic approaches (Mökander et al., 2021). One of the central ethical issues raised by such complexity is the opacity of the resulting decision processes (Pasquale, 2015;Rahman, 2021). ...
Algorithmic decision-making systems (ADMS) are increasingly being used by public and private organizations to enact decisions traditionally made by human beings across a broad range of domains, including business, law enforcement, education, and healthcare. Their growing prevalence engenders profound ethical challenges, which, we maintain, should be examined in a structured and theoretically informed fashion. However, much of the ethical exploration of ADMS within the IS field draws upon an atheoretical application of ethics. In this paper, we argue that the “big three” ethical theories of consequentialism, deontology, and virtue ethics can inform a structured comparative analysis of the ethical significance of ADMS. We demonstrate the value of such an approach through an illustrative case study of an ADMS in use by an Australian bank. Building upon this analysis, we address four characteristics of ADMS from the three theoretical perspectives, provide guidance on the contexts within which the application of each theory might be particularly fruitful, and highlight the advantages of theoretically grounded ethical analyses of ADMS.
... These challenges and risks related to AI systems underscore the importance of AI governance at the organizational, interorganizational, and societal levels (Laato et al., 2022;Mäntymäki et al., 2022a, b;Minkkinen et al., 2022a, b;Schneider et al., 2022;Seppälä et al., 2021). As a closely related parallel to governance, auditing of AI is promoted as a means of tackling risks by holding AI systems and organizations that use AI to certain criteria and by requiring necessary controls (Koshiyama et al., 2021;Minkkinen et al., 2022a, b;Mökander et al., 2021;Sandvig et al., 2014). In addition to tackling risks, auditing of AI has been promoted as a new industry and a source of economic growth (Koshiyama et al., 2021). ...
... To address the paucity of the CAAI literature, this study has been positioned to answer the following research question: What is continuous auditing of artificial intelligence, and what frameworks and tools exist for its execution? The current paper advances the body of knowledge on auditing of AI (Brown et al., 2021;Koshiyama et al., 2021;Mökander et al., 2021;Sandvig et al., 2014) in two ways. First, we connect the research on auditing of AI and CA, introducing the CAAI concept. ...
... The recent literature has introduced the concept of the ethics-based auditing (EBA) of automated decision-making systems (Mökander et al., 2021). EBA is defined as "a structured process whereby an entity's present or past behaviour is assessed for consistency with relevant principles or norms" (Mökander et al., 2021, p. 1). ...
Full-text available
Artificial intelligence (AI), which refers to both a research field and a set of technologies, is rapidly growing and has already spread to application areas ranging from policing to healthcare and transport. The increasing AI capabilities bring novel risks and potential harms to individuals and societies, which auditing of AI seeks to address. However, traditional periodic or cyclical auditing is challenged by the learning and adaptive nature of AI systems. Meanwhile, continuous auditing (CA) has been discussed since the 1980s but has not been explicitly connected to auditing of AI. In this paper, we connect the research on auditing of AI and CA to introduce CA of AI (CAAI). We define CAAI as a (nearly) real-time electronic support system for auditors that continuously and automatically audits an AI system to assess its consistency with relevant norms and standards. We adopt a bottom-up approach and investigate the CAAI tools and methods found in the academic and grey literature. The suitability of tools and methods for CA is assessed based on criteria derived from CA definitions. Our study findings indicate that few existing frameworks are directly suitable for CAAI and that many have limited scope within a particular sector or problem area. Hence, further work on CAAI frameworks is needed, and researchers can draw lessons from existing CA frameworks; however, this requires consideration of the scope of CAAI, the human–machine division of labour, and the emerging institutional landscape in AI governance. Our work also lays the foundation for continued research and practical applications within the field of CAAI.
... Several impactful lines of work do not consider reliability (Hagendorff 2020;Langenkamp et al. 2020;Metcalf et al. 2021;Sandvig et al. 2014;Sühr et al. 2021;Venkatadri et al. 2018;Wilson et al. 2021). Of the works that do take reliability under consideration, some refer to this concept as stability (Brown et al. 2021;Koshiyama et al. 2021;Robertson et al. 2018;Sloane et al. 2022;Riksrevisjonen 2020), others as reliability (Fjeld et al. 2020;Mökander et al. 2021;Raji et al. 2020;Riksrevisjonen 2020;Shneiderman 2020), and others yet as robustness (Chen et al. 2018;Fjeld et al. 2020;Mökander et al. 2021;Oala et al. 2020;ORCAA 2020). Bandy Bandy (2021) forgoes specific terminology and simply refers to changes to input and output. ...
... Several impactful lines of work do not consider reliability (Hagendorff 2020;Langenkamp et al. 2020;Metcalf et al. 2021;Sandvig et al. 2014;Sühr et al. 2021;Venkatadri et al. 2018;Wilson et al. 2021). Of the works that do take reliability under consideration, some refer to this concept as stability (Brown et al. 2021;Koshiyama et al. 2021;Robertson et al. 2018;Sloane et al. 2022;Riksrevisjonen 2020), others as reliability (Fjeld et al. 2020;Mökander et al. 2021;Raji et al. 2020;Riksrevisjonen 2020;Shneiderman 2020), and others yet as robustness (Chen et al. 2018;Fjeld et al. 2020;Mökander et al. 2021;Oala et al. 2020;ORCAA 2020). Bandy Bandy (2021) forgoes specific terminology and simply refers to changes to input and output. ...
Full-text available
Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers’ resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Crucially, rather than challenging or affirming the assumptions made in psychometric testing — that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job — we frame our audit methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. Our main contribution is the development of a socio-technical framework for auditing the stability of algorithmic systems. This contribution is supplemented with an open-source software library that implements the technical components of the audit, and can be used to conduct similar stability audits of algorithmic systems. We instantiate our framework with the audit of two real-world personality prediction systems, namely, Humantic AI and Crystal. The application of our audit framework demonstrates that both these systems show substantial instability with respect to key facets of measurement, and hence cannot be considered valid testing instruments.
... AI audits, even more than standard technological or financial audits, fall into the field of ethics because of the peculiar characteristic of artificial systems to have agency, namely, to perform "morally qualifiable actions" (Floridi, 2013, p. 134). Mökander and Floridi (2021) introduced the definition of ethics-based audits (EBA) and soon afterwards developed the procedural features of such EBA (Mökander et al., 2021b). According to the authors, the procedures of EBA should be holistic, traceable, accountable, strategic, dialectic, continuous, and drive redesign. ...
AI has become a hot topic both among the fervent integrators and the terrified apocalyptics; the formers see AI as the ultimate panacea, while the latter look at it as a great danger. In between, there are several organisations and individuals who consider AI to be good for humanity provided it respects certain limits. Collaboration and contributing to international associations and private firms, these people address the problem by trying to mitigate some low-level errors, such as negative biases, or high-level ones, such as problems of accountability and governance. Each of these bodies works towards a goal: reducing, through regulations, standards, advice, assurances, and independent audits. A particular phenomenon has been observed: internationally, ethical principles and approaches to risk reduction are becoming increasingly similar. Based on this assumption, the authors introduce an analogy to understand the ongoing synchronisation effect. Aware that an ultimate alignment will be impossible (due to the tendency to incompleteness of ethical reasoning) and strongly discouraged (due to the tendency of universal systems to flatten the peripheral voices), the authors suggest a theoretical investigation of the phenomenon of synchronisation and invite to feed it practically through the available tools of data governance. They show how ethics-based audits can be a suitable tool for the task through the presentation of a case study.
... The automated process normally follows various action steps and predefined business rules, yet intelligent automation flows may include unambiguous behaviors that have low cognitive requirements (Romao et al., 2019). It certainly evolves onto direction of the automated, intelligent decision-making systems that are selflearning and gather process data to make judgements without human involvement at a case (Mökander et al., 2021). Being a multidimensional and multiperspective phenomenon, AI is expected to outperform human labor (Coombs et al., 2020), due to its recent developments in simulating human behavior to attempt conducting same processes, classify data, predict outcomes, make decisions, or detect similar information than human employees (Polak et al., 2020). ...
Conference Paper
Full-text available
Constantly changing technologies affect not only the value types that companies generate but also their processes and their most important resource: employees. Such transitions referred to as digital transformation, mean that employees must tailor their skills and ways of working to stay considered a long-term asset for their company. Intelligent information systems, such as robotic process automation (RPA), are gradually taking over employees' work efforts, and thus replacing part of the human workforce. In the paper, we investigate whether non-technical employees (in this case, accountants) can be retrained to work as entry-level RPA software developers. In our case study (observations blended with interviews), we show how retraining transforms employees' identity, addresses their anxiety and skepticism, and impacts the organization. Further, we show the relevance of domain knowledge, digital literacy, and the cornerstones of a training program transforming accountants into RPA developers.
... At the same time, it comes with substantial risks-especially if such systems are not introduced 0000-0000/00$00.00 © 2021 IEEE and deployed in a careful manner.' 1 Given the intended penetration of ADM systems in the social tissue, the role of the context in these systems should be carefully analysed, since they may raise ethical concerns [5]. ...
Decisions made in areas such as economics, engineering, industry and medical sciences are usually based on finding and interpreting solutions to optimisation problems. When modelling an optimisation problem, it should be clear that people do not make decisions in a vacuum or in isolation from the reality. So, there is always a decision-making context that, in addition to the natural constraints of the problem, acts as a filter on the candidate solutions available. If this fact is omitted, optimal but useless solutions to the problem can be obtained. In this paper, we propose a systematic way of modelling contexts based on fuzzy propositions and two approaches ( a priori and a posteriori ) for solving optimisation problems under their influence. In the proposed a priori approach, the context is explicitly included in the mathematical model of the problem. As this approach may have a limited application due to the increasing number of constraints and their nature, an a posteriori approach is proposed, in which a set of solutions, obtained by any means (like exact algorithms, simulation or metaheuristics), are checked for their suitability to the context by using a multi-criteria decision-making methodology. A simple fish harvesting problem in a sustainability context and a tourist trip design problem in a pandemic context were solved for illustration purposes. Our results provide researchers and practitioners with a methodology for more effective optimisation and decision-making.
On 21 April 2021, the European Commission published the proposal of the new EU Artificial Intelligence Act (AIA)—one of the most influential steps taken so far to regulate AI internationally. This chapter highlights some foundational aspects of the Act and analyses the philosophy behind its proposal.KeywordsArtificial IntelligenceEuropean CommissionGovernanceLegislation
The European Guidelines on Trustworthy Artificial Intelligence refer to auditing as a key way to implement ethical practices into the development and deployment of artificial intelligence (AI). However, auditing AI, and especially the “ethics audit” (EA, also known as the business ethics audit, social audit, or corporate social responsibility audit) of AI, is still a vague concept. It is unclear what should be the object of the audit – whether the processes used to develop an AI system or the system’s use and real-world application – as well as which aspects of AI systems should be audited – for example, whether the auditing of AI should focus on risk, accountability, or governance. This chapter aims to shed light on EA of AI by analysing the existing relevant literature on auditing information technologies (IT). By using a qualitative evidence synthesis, a method that employs selective or purposive sampling in order to identify ‘themes’ or ‘constructs’ from the literature, this chapter reviews methods for auditing IT, with a particular focus on methodologies connected to three key concepts: governance, assurance, and risk. Its goals are to identify a set of methodologies and standards that can be a source of reference for the AI community when developing EA protocols for AI; and to clarify important lessons and considerations.KeywordsArtificial intelligenceEthicsIT auditGovernanceQuality assuranceRisk management
Full-text available
A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation.
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As the use of data and artificial intelligence systems becomes crucial to core services and business, it increasingly demands a multi-stakeholder and complex governance approach. The Information Commissioner's Office’s ‘Guidance on the AI auditing framework: Draft guidance for consultation’ is a move forward in AI governance. The aim of this initiative is toward producing guidance that encompasses both technical (e.g. system impact assessments) and non-engineering (e.g. human oversight) components to governance and represents a significant milestone in the movement towards standardising AI governance. This paper will summarise and critically evaluate the ICO effort and try to anticipate future debates and present some general recommendations.
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In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed.
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In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making (ADM) environments. With the rise of artificial intelligence (AI) and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant con-textual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought.
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Flawed scholarship threatens to mislead the public and stymie future research by compromising ML’s intellectual foundations. Indeed, many of these problems have recurred cyclically throughout the history of AI and, more broadly, in scientific research. In 1976, Drew McDermott chastised the AI community for abandoning self-discipline, warning prophetically that "if we can’t criticize ourselves, someone else will save us the trouble." The current strength of machine learning owes to a large body of rigorous research to date, both theoretical and empirical. By promoting clear scientific thinking and communication, our community can sustain the trust and investment it currently enjoys.
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This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify ‘true’ moral principles for AI; rather, it is to identify fair principles for alignment that receive reflective endorsement despite widespread variation in people’s moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified.
The concept of distributed moral responsibility (DMR) has a long history. When it is understood as being entirely reducible to the sum of (some) human, individual and already morally loaded actions, then the allocation of DMR, and hence of praise and reward or blame and punishment, may be pragmatically difficult, but not conceptually problematic. However, in distributed environments, it is increasingly possible that a network of agents, some human, some artificial (e.g. a program) and some hybrid (e.g. a group of people working as a team thanks to a software platform), may cause distributed moral actions (DMAs). These are morally good or evil (i.e. morally loaded) actions caused by local interactions that are in themselves neither good nor evil (morally neutral). In this article, I analyse DMRs that are due to DMAs, and argue in favour of the allocation, by default and overridably, of full moral responsibility (faultless responsibility) to all the nodes/agents in the network causally relevant for bringing about the DMA in question, independently of intentionality. The mechanism proposed is inspired by, and adapts, three concepts: back propagation from network theory, strict liability from jurisprudence and common knowledge from epistemic logic.
This book predicts the decline of today's professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want doctors, teachers, accountants, architects, the clergy, consultants, lawyers, and many others, to work as they did in the 20th century. The Future of the Professions explains how 'increasingly capable systems' -- from telepresence to artificial intelligence -- will bring fundamental change in the way that the 'practical expertise' of specialists is made available in society. The authors challenge the 'grand bargain' -- the arrangement that grants various monopolies to today's professionals. They argue that our current professions are antiquated, opaque and no longer affordable, and that the expertise of their best is enjoyed only by a few. In their place, they propose six new models for producing and distributing expertise in society. The book raises important practical and moral questions. In an era when machines can out-perform human beings at most tasks, what are the prospects for employment, who should own and control online expertise, and what tasks should be reserved exclusively for people? Based on the authors' in-depth research of more than ten professions, and illustrated by numerous examples from each, this is the first book to assess and question the relevance of the professions in the 21st century.