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Recommender Systems and their Ethical Challenges

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This article presents the first, systematic analysis of the ethical challenges posed by recommender systems. Through a literature review, the article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders-as opposed to just the receivers of a recommendation-in assessing the ethical impacts of a recommender system.
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Recommender Systems and their Ethical Challenges
Silvia Milano1*, Mariarosaria Taddeo1,2, Luciano Floridi1,2
1Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, United Kingdom
2The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom.
* Corresponding author. Email:
This article presents the first, systematic analysis of the ethical challenges posed by recommender
systems. Through a literature review, the article identifies six areas of concern, and maps them
onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the
literature: currently user-centred approaches do not consider the interests of a variety of other
stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical
impacts of a recommender system.
Algorithms; Artificial Intelligence; Digital Ethics; Ethical Trade-offs; Ethics of Recommendation;
Machine Learning; Recommender Systems.
This work was supported by Privacy and Trust Stream - Social lead of the PETRAS Internet of
Things research hub. PETRAS is funded by the Engineering and Physical Sciences Research
Council (EPSRC), grant agreement no. EP/N023013/1; and Google UK Limited.
We interact with recommender (or recommendation) systems (RS) on a regular basis, when we use
digital services and apps, from Amazon to Netflix and news aggregators. They are algorithms that
make suggestions about what a user may like, such as a specific movie. Slightly more formally, they
are functions that take information about a user’s preferences (e.g. about movies) as an input, and
output a prediction about the rating that a user would give of the items under evaluation (e.g., new
movies available). We shall say more about the nature of recommender systems in the following
pages, but even this general description suffices to clarify that, in order to work effectively and
efficiently, recommender systems collect, curate, and act upon vast amounts of personal data.
Inevitably, they end up shaping individual experience of digital environments and social
interactions (Burr, Cristianini, & Ladyman, 2018; de Vries, 2010; Karimi, Jannach, & Jugovac,
RS are ubiquitous and there is already much technical research about how to develop ever
more efficient systems (Adomavicius & Tuzhilin, 2005; Jannach & Adomavicius, 2016; Ricci,
Rokach, & Shapira, 2015). In the past 20 years, RS have been developed focusing mostly on
business applications, and the emphasis has tended to be on commercial objectives. But RS have
a wider impact on users and on society more broadly. After all, they shape user preferences and
guide choices, both individually and socially. This impact is significant and deserves ethical
scrutiny, not least because RS can also be deployed in contexts that are morally loaded, such as
health care, lifestyle, insurance, and the labour market. Clearly, whatever the ethical issues may be,
they need to be understood and addressed by evaluating the design, deployment and use of the
recommender systems, and the trade-offs between the different interests at stake. A failure to do
so may lead to opportunity costs as well as problems that could otherwise be mitigated or avoided
altogether, and, in turn, to public distrust and backlash against the use of RS in general (Koene et
al., 2015).
Research into the ethical issues posed by RS is still in its infancy. The debate is also
fragmented across different scientific communities, as it tends to focus on specific aspects and
applications of these systems in a variety of contexts. The current fragmentation of the debate may
be due to two main factors: the relative newness of the technology, which took off with the spread
of internet-based services and the introduction of collaborative filtering techniques in the 1990s
(Adomavicius & Tuzhilin, 2005; Pennock, Horvitz, & Giles, 2000); and the proprietary and privacy
issues involved in the design and deployment of this class of algorithms. The details of RS currently
in operation are treated as highly guarded industrial secrets. This makes it difficult for independent
researchers to access information about their internal operations, and hence provide any evidence-
based assessment. In the same vein, due to privacy concerns, providers of recommendation
systems may be reluctant to share information that could compromise their users’ personal data
(Paraschakis, 2018).
Against this background, this article addresses both problems (infancy and fragmentation),
by providing a survey of the current state of the literature, and by proposing an overarching
framework to situate the contributions to the debate. The overall goal is to reconstruct the whole
debate, understand its main issues, and hence offer a starting point for better ways of designing
RS and regulating their use.
2.!A Working Definition of Recommender Systems
The task of a recommendation system – i.e. what we shall call the recommendation problemis often
summarized as that of finding good items (Jannach & Adomavicius, 2016). This description is
common and popular among practitioners, especially in the context of e-commerce applications.
However, it is too broad and not very helpful for research purposes. To make it operational one
needs to specify, among other things, three parameters:
a)!what the space of options is;
b)!what counts as a good recommendation; and, importantly
c)!how the RS’s performance can be evaluated.
Specifying these parameter choices is highly dependent on the domain of application and the level
of abstraction (LoAs, see (Floridi, 2016))
from which the problem is considered (Jannach, Zanker,
Ge, & Gröning, 2012). Typically, the literature implements three LoAs: catalogue-based, decision
support, and multi-stakeholder environment. Let us consider each of these in turn.
In e-commerce applications, the space of options (that is, the observables selected by the
LoA) may be the items in the catalogue, while a good recommendation may be specified as one
which ultimately results in a purchase. To evaluate the system performance, one may compare the
RS’s predictions to the actual user behaviour after a recommendation is made. In the domain of
news recommendations, a good recommendation may be defined as a news item that is relevant to
the user (Floridi, 2008), and one may use click-through rates as a proxy to evaluate the accuracy of
the system’s recommendations. Similar RS are designed to develop a model of individual users and
to use it to predict the users’ feedback on the system’s recommendation, which is essentially a
prediction problem.
A level of abstraction can be imagined as an interface that enables one to observe some aspects of a s system analysed,
while making other aspects opaque or indeed invisible. For example, one may analyse a house at the LoA of a buyer,
of an architect, of a city planner, of a plumber, and so on. LoAs are common in computer science, where systems are
described at different LoAs (computational, hardware, user-centred etc.). LoAs can be combined in more complex
sets, and can be, but are not necessarily always, hierarchical.
Taking a different LoA, RS may also be considered to provide decision support to their users.
For example, an online booking RS may be designed to facilitate the user’s choice of hotel options.
In this case, defining what counts as a good recommendation is more complex, because it involves
appreciation of the user’s goals and decision-making abilities. Evaluating the system’s performance
as a decision support requires more elaborate metrics. For example, (Jameson et al., 2015) consider
six strategies for generating recommendations, which track different choice patterns based on
either of the following features: (1) the attributes of the options; (2) the expected consequences of
choosing an option; (3) prior experience with similar options; (4) social pressure or social
information about the options; (5) following a specific policy; (6) trial-and-error based choice.
More recently, (Abdollahpouri, Burke, & Mobasher, 2017) have proposed a different kind
of LoA (our terminology), defining RS in terms of multi-stakeholder environments (what we would
call the LoA’s observables), where multiple parties (including users, providers, and system
administrators) can derive different utilities from recommendations. Epistemologically, this
approach is helpful because it enables one to conceptualise explicitly the impact that RS have at
different levels, both on the individual users interacting with them, and on society more broadly,
making it possible to articulate what ethical trade-offs could be made between these different,
possibly competing interests.
In view of the previous LoAs, and for the purposes of this article, we take recommender
systems to be a class of algorithms that address the recommendation problem using a content-based or
collaborative filtering approach, or a combination thereof. This choice has three advantages. It is
compatible with the most common LoAs we have listed above. By focusing on the algorithmic
nature of recommender systems, it also singles out one of the fastest growing areas of research
and applications for machine learning. And it enables us to narrow down the scope of the study,
as we shall not consider systems that approach the recommendation problem using different
techniques, such as, for instance, expert systems like IBM Watson. With these advantages in mind,
in the next section we propose a general taxonomy to identify the ethical challenges of RS. In
section 4 we review the current literature, structured around six areas of concern. We conclude in
section 5, by mapping the discussion onto our ethical taxonomy and indicating the direction of
our further work in the area.
3.!How to Map the Ethical Challenges Posed by Recommender Systems
In order to identify what is ethically at stake in the design and deployment of a RS, let us start with
a formal taxonomy. This is how we propose to design it.
The question about which moral principles may be correct is deeply contentious and
debated in philosophy. Fortunately, in this article we do not have to take a side because all we need
is a distinction about which there is a general consensus: there are at least two classes of variables
that are morally relevant, actions and consequences. Of course, other things could also be morally
relevant, in particular intentions. However, for our purposes, the aforementioned distinction is all
we need, so we shall assume that a recommender system’s behaviour and impact will suffice to
provide a clear understanding of what is ethically at stake.
The value of some consequences is often measured in terms of the utility they contain. So,
it is reasonable to assume that any aspect of a RS that could impact negatively the utility of any of
its stakeholders, or risk imposing such negative impacts, constitutes a feature that is ethically
While the concept of utility can be made operational using quantifiable metrics, rights are
usually taken to provide qualitative constraints on actions. Thinking in terms of actions and
consequences, we can identify two ways in which a recommender system can have ethical impacts.
First, its operations can
a)!impact (negatively) the utility of any of its stakeholders; and/or
b)!violate their rights.
Second, these two kinds of ethical impact may be immediate—for example, a recommendation may
be inaccurate, leading to a decrease in utility for the useror they may expose the relevant parties
to future risks. The ethics of risk imposition is the subject of a growing philosophical literature,
which highlights how most activities involve imposition of risks (Hansson, 2010; Hayenhjelm &
Wolff, 2012). In the case of RS, for example, the risks may involve exposing users to undue privacy
violations by external actors, or the exposure to potentially irrelevant or damaging content.
Exposure to risks of these sorts can constitute a wrong, even if no adverse consequences actually
Given the previous analysis, we may now categorise the ethical issues caused by
recommender systems along two dimensions (see Table 1):
i)!whether a (given feature of a) RS negatively impacts the utility of some of its
stakeholders or, instead, constitutes a rights violation, which is not necessarily
measured in terms of utility; and
ii)!whether the negative impact constitutes an immediate harm or it exposes the relevant
party to future risk of harm or rights violation.
The idea that exposing someone to risks can constitute a wrong to them, even if the adverse consequences fail to
materialise, is familiar from other contexts, e.g. medical ethics: for example, negligence in treating a patient constitutes
a wrong, even if the patient ultimately recovers and does not suffer as a result of the negligence.
Table 1 summarises our proposed taxonomy, including some examples of different types of ethical
impacts of recommender systems, to be discussed in section 5.
Table 1
Immediate Harm
Exposure to Risk
e.g. inaccurate recommendations
e.g. A/B testing (see section 4.1)
e.g. unfair treatment
e.g. leaking of sensitive information
With the help of this taxonomy we are now ready to review the contributions provided by the
current literature. We shall offer a general discussion of our findings in the conclusion.
4.!The Ethical Challenges of Recommender Systems
The literature addressing the ethical challenges posed by RS is sparse, with the discussion of
specific issues often linked to a specific instance of a RS, and appearing to be fragmented across
disciplinary divides. Through a multidisciplinary, comparative meta-analysis, we identified six main
areas of ethical concerns (see appendix for our methodology). They often overlap but, for the sake
of clarity, we shall analyse them separately in the rest of this section.
4.1.!Ethical content
Only a handful of studies to date address explicitly the ethics of RS as a specific issue in itself.
Earlier work on the question of ethical recommendations focuses more on the content of the
recommendations, and proposes ways to filter the items recommended by the system on the basis
of cultural and ethical preferences. Four studies are particularly relevant. (Souali, El Afia, & Faizi,
2011) consider the issue of RSs that are not culturally appropriate, and propose an “ethical
database”, constructed on the basis of what are taken to be a region’s generally accepted cultural
norms, which act as a filter for the recommendations. (Tang & Winoto, 2016) take a more dynamic
approach to the issue, proposing a two-layer RS, comprising a user-adjustable “ethical filter” that
screens the items that can be recommended based on the user’s specified ethical preferences.
(Rodriguez & Watkins, 2009) adopt a more abstract approach to the problem of ethical
recommendations, proposing a vision for a eudaimonic RS, whose purpose is to “produce societies
in which the individuals experience satisfaction through a deep engagement in the world”. This,
the authors predict, could be made achievable through the use of interlinked big data structures.
Finally, (Paraschakis, 2016, 2017, 2018) provides one of the most detailed accounts.
Focusing on e-commerce applications, Paraschakis suggests that there are five ethically
problematic areas:
!the practices of user profiling,
!data publishing,
!algorithm design,
!user interface design, and
!online experimentation or A/B testing, i.e. the practice of exposing selected groups of
users to modifications of the algorithm, with the aim of gathering feedback on the
effectiveness of each version from the user responses.
The risks he identifies relate to breaches of a user’s privacy (e.g. via data leaks, or by data gathering
in the absence of explicit consent), anonymity breaches, behaviour manipulation and bias in the
recommendations given to the user, content censorship, exposure to side effects, and unequal
treatment in A/B testing with a lack of user awareness, leading to a lack of trust. The solutions put
forward in (Paraschakis, 2017) revolve around a user-centred design approach (more on this in the
next paragraph), introducing adjustable tools for users to control explicitly the way in which RS
use their personal data, in order to filter out marketing biases or content censorship, and to opt
out of online experiments.
With the exception of (Souali et al., 2011), who adopt a recommendation filter based on
geographically-located cultural norms, the solutions described in this section rely on a user-centred
approach. Recalling our taxonomy, they try to minimise the negative impact on the user’s utility
in particular, unwanted exposure to testing, and inaccurate recommendations—and on the user’s
rights, in particular, recommendations that do not agree with the user’s values, or expose them to
privacy violations. However, user-centred solutions have significant shortcomings: they may not
transfer to other domains, they may be insufficient to protect the user’s privacy, and they may
result in inefficiency, for example impairing the system’s effectiveness in generating new
recommendations, if enough users choose to opt out of profile tracking or online testing.
Moreover, users’ choice of parameters can reveal sensitive information about the users themselves.
For example, adding a filter to exclude some kind of content gives away the information that the
user may find this content distressing, irrelevant, or in other ways unacceptable. But above all, the
main problem is that, although user-centred solutions may foster the transparency of
recommender systems, they also shift the responsibility and accountability for the protection of
rights and utility to the users. These points highlight how user-centred solutions in general are
challenged by their demanding nature, as they may constitute a mere shift in responsibility when
the users are only nominally empowered but actually unable to manage all the procedures needed
to protect their interests. This may therefore be an unfair shift, since it places undue burdens on
the users, and is in any case problematic because the effectiveness of these solutions varies with
the level of awareness and expertise of the users themselves, which may lead to users experiencing
different levels of protection depending on their ability to control the technology.
Implementing an “ethical filter” for a recommender system, as suggested by (Rodriguez &
Watkins, 2009), would also be controversial in some applications, for example if it were used by a
government to limit citizensability to access some politically sensitive contents. As for the
eudaimonic approach, this goes in the direction of designing a recommender system that is an
optimal decision support, yet it seems practically unfeasible, and at least much more research would
be needed. Figuring out what is a “good human life” is something that millennia of reflection have
not yet solved.
User privacy is one of the primary challenges for recommendation systems (Friedman et al., 2015;
Koene et al., 2015; Paraschakis, 2018). This may be seen as inevitable, given that a majority of the
most commercially successful recommender systems are based on hybrid or collaborative filtering
techniques, and work by constructing models of their users in order to generate personalised
recommendations. Privacy risks occur in at least four stages. First, they can arise when data are
collected or shared without the user’s explicit consent. Second, once data sets are stored, there is
the further risk that they may be leaked to external agents, or become subject to de-anonymization
attempts (Narayanan, 2008). At both stages, privacy breaches expose users to risks, which may
result in loss of utility (for example, if individual users are targeted by malicious agents as a result),
or in rights violations (for example, if users’ private information is utilised in ways that threaten
their individual autonomy, see section 4.3 below). Third, and independently of how securely data
are collected and stored, privacy concerns also arise at the stage of inferences that the system can
(enable one to) draw from the data. Users may not be aware of the nature of these inferences, and
they may object to this use of their personal data if they were better informed. Privacy risks do not
only concern data collection because, for example, an external agent observing the
recommendation that the system generates for a given user may be able to infer some sensitive
information about the user (Friedman et al., 2015). Extending the notion of informed consent to
For a critical analysis of empowerment see Jessica Morley and Luciano Floridi (forthcoming), “Against
Empowerment: How to Reframe the Role of mHealth Tools in the Healthcare Ecosystem”.
the indirect inferences from user recommendations appears difficult.
Finally, there is also another
subtle, but important, systemic issue regarding privacy, which arises at the stage of collaborative
filtering: the system can construct a model of the user based on the data it has gathered on other
users’ interactions. In other words, as long as enough users interact and share their data with the
system, the system may be able to construct a fairly accurate profile even for those users about
whom it has fewer data. This indicates that it may not be feasible for individual users to be shielded
completely from the kinds of inferences that the system may be able to draw about them. It could
be a positive feature in some domains, like medical research, but it may also turn out to be
problematic in other domains, like recruitment or finance.
Current solutions to the privacy challenges intrinsic to recommender systems (especially
those based on collaborative filtering techniques) fall into three broad categories, covering
architectures, algorithmic, and policy approaches (Friedman et al., 2015). Privacy-enhancing
architectures aim to mitigate privacy risks by storing user data in separate and decentralised
databases, to minimise the risk of leaks. Algorithmic solutions focus on using encryption to
minimise the risk that user data could be exploited by external agents for unwarranted purposes.
Policy approaches, including GDPR legislation, introduce explicit guidelines and sanctions to
regulate data collection, use, and storage.
The user-centred recommendation framework proposed by (Paraschakis, 2017), which we
already encountered in the previous section, also introduces explicit privacy controls, letting the
users decide whether their data can be shared, and with whom. However, as we have already
remarked, user-centred approaches have limits, as they may constitute a mere shift in responsibility,
placing an undue burden on the users. A possible issue that may arise specifically with user-enabled
privacy controls is that the user’s privacy preferences would, in themselves, constitute informative
metadata, which the system (or external observers) could use to make sensitive inferences about
the user, for example, to infer that a user who has strong privacy settings may have certain
psychological traits, or that they may have “something to hide”. When considering systemic
inferences, due to the nature of collaborative filtering methods, even if user-centred adjustments
could be implemented across the board in effective ways, they would arguably still not solve the
Crucially, due to the nature of recommender systems which, as we have seen, rely on
user models in order to generate personalised recommendations any approach to the issue of
user privacy will need to take into account the likely trade-off between privacy and accuracy, but
The recent ProPublica/Facebook exchange about auditing targeted ads may configure as a privacy breach of this
kind (Merrill & Tobin, 2019).
also fairness and explainability of algorithms (Friedman et al., 2015; Koene et al., 2015). For this
reason, ethical analyses of recommender systems are better developed by embracing a macro-
ethical approach. This is an approach that is able to consider specifically ethical problems related
to data, algorithms, and practices, but also how the problems relate, depend on, and impact each
other (Floridi & Taddeo, 2016).
4.3.!Autonomy and Personal Identity
Recommender systems can encroach on individual users’ autonomy, by providing
recommendations that nudge users in a particular direction, by attempting to “addict” them to
some types of contents, or by limiting the range of options to which they are exposed (Burr et al.,
2018; de Vries, 2010; Koene et al., 2015; Taddeo & Floridi, 2018). These interventions can range
from being benign (enabling individual agency and supporting better decision making by filtering
out irrelevant options), to potentially questionable (persuasion, nudging), to possibly malign (being
manipulative and coercive (Burr et al., 2018)).
Algorithmic classification used to construct user models on the basis of aggregate user data
can reproduce social categories. This may introduce bias in the recommendations. We shall discuss
this risk in detail in the next section (4.4). Here, the focus is on a distinctive set of issues arising
when the algorithmic categorization of users does not follow recognisable social categories. (de
Vries, 2010) powerfully articulates the idea that our experience of personal identity is mediated by
the categories to which we are assigned. Algorithmic profiling, performed by recommender
systems, can disrupt this individual experience of personal identity, for at least two main reasons.
First, the recommender system’s model of each user is continuously reconfigured on the basis of
the feedback provided by other users’ interactions with the system. In this sense, the system should
not be conceptualised as tracking a pre-established user identity and tailoring its recommendations
to it, but rather as contributing to the construction of the user identity dynamically (Floridi, 2011).
Second, the labelling that the system uses to categorise users may not correspond to recognisable
attributes or social categories with which the user would self-identify (for example, because
machine-generated categories may not correspond to any known social representation), so even if
users could access the content of the model, they would not be able to interpret it and connect it
with their lived experiences in a meaningful way. These features of recommender systems create
an environment where personalization comes at the cost of removing the user from the social
categories that help mediate their experiences of identity.
In this context, an interesting take on the issue of personal autonomy in relation to
recommender systems comes from the “captology” of recommender systems. (Seaver, 2018a)
develops this concept from an anthropological perspective:
[a]s recommender[s] spread across online cultural infrastructures and become practically
inescapable, thinking with traps offers an alternative to common ethical framings that
oppose tropes of freedom and coercion (Seaver, 2018a).
Recommender systems appear to function as “sticky traps(our terminology) insofar as they are
trying to “glue” their users to some specific solutions. This is reflected in what Seaver calls
“captivation metrics (i.e. that measure user retention), which are commonly used by popular
recommender systems. A prominent example is YouTube’s recommendation algorithm, which
received much attention recently for its tendency to promote biased content and “fake news”, in
a bid to keep users engaged with its platform (Chaslot, 2018). Regarding recommender systems as
traps requires engaging with the minds of the users: traps can only be effective if their creators
understand and work with the target’s world view and motivations, so the autonomous agency of
the target is not negated, but effectively exploited. Given this captological approach, and given the
effectiveness and ubiquity of the traps of recommender systems, the question to ask is not how
users can escape from them, but rather how users can make the traps work for them.
In theory, explaining how personalised recommendations are generated for individual users could
help to mitigate the risk of encroaching on their autonomy, giving them access to the reasons why
the system “thinks” that some options are relevant to them. It would also help increase the
transparency of the algorithmic decisions concerning how to class and model users, thus helping
to guard against bias.
Designing and evaluating explanations for recommender systems can take different forms,
depending on the specific applications. As reported by (Tintarev & Masthoff, 2011), several studies
have pursued a user-centred approach to evaluation metrics, including metrics to evaluate
explanations of recommendations. What counts as a good explanation depends on several criteria:
the purpose of the recommendation for the user; whether the explanation accurately matches the
mechanism by which the recommendation is generated; whether it improves the system’s
transparency and scrutability; and whether it helps the user to make decisions more efficiently (e.g.
more quickly), and more effectively, e.g. in terms of increased satisfaction.
These criteria are satisfied by factual explanations.
However, factual explanations are
notoriously difficult to achieve. As noted by (Herlocker, Konstan, & Riedl, 2000),
recommendations generated by collaborative filtering techniques can, on a simple level, be
conceptualised as analogous to “word of mouth” recommendations among users. However,
offline word of mouth recommendations can work on the basis of trust and shared personal
experience, whereas in the case of recommender systems users do not have access to the identity
of the other users, nor do they have access to the models that the system uses in order to generate
the recommendations. As we mentioned, this is an issue in so far as it diminishes the user’s
autonomy. It may be difficult to provide good factual explanations in practice also for
computational reasons (the required computation to generate a good explanation may be too
complex), and because they may have distorting effects on the accuracy of the recommendations
(Tintarev & Masthoff, 2011). For example, explaining to a user that a certain item is recommended
because it is the most popular with other users may increase the item’s desirability, thus generating
a self-reinforcing pattern where the item will be recommended more often because it is popular.
This, in turn, reinforces its popularity, ending in a winner-takes-all scenario that, depending on the
intended domain of application, can have negative effects on the variety of options, plurality of
choices, and the emergence of competition (Germano, mez, & Mens, 2019). Arguably, this may
be one of the reasons why Amazon does not automatically privilege products with less than perfect
scoring but that have been rated by a large number of reviewers.
Fairness in algorithmic decision making is a wide-ranging issue, made more complicated by the
existence of multiple notions of fairness, which are not all mutually compatible (Friedler,
Scheidegger, & Venkatasubramanian, 2016). In the context of recommender systems, several
articles identified in this review address the issue of recommendations that may reproduce social
biases. They may be synthesised around two approaches.
On the one hand, (Yao & Huang, 2017) consider several possible sources for unfairness
in collaborative filtering, and introduce four new metrics to address them by measuring the
distance between recommendations made by the system to different groups of users. Focusing on
Factual explanations are usually contrasted to counterfactual ones, that describe what would have had to be the case,
in order for a certain state or outcome (different from the actual one) to occur. For example, suppose that while
browsing an e-commerce website, Alice is recommended a brand of dog food. A counterfactual explanation of why
Alice received this recommendation would specify what would have had to be the case, for Alice not to be
recommended this specific product (for example, had she not browsed dog collars, she would not have been
recommended dog food). A factual explanation, on the other hand, would specify why this specific item was
recommended, for example why this specific brand of dog food was deemed good for Alice.
collaborative filtering techniques, they note that these methods assume that the missing ratings
(i.e., the ones that the system needs to infer from the statistical data to predict a user’s preferences)
are randomly distributed. However, this assumption of randomness introduces a potential source
of bias in the system’s predictions, because it is well documented that users’ underlying preferences
often differ from the sampled ratings, since the latter are affected by social factors, which may be
biased (Marlin, Zemel, Roweis, & Slaney, 2007). Following (Yao & Huang, 2017), (Farnadi, Kouki,
Thompson, Srinivasan, & Getoor, 2018) also identify the two primary sources of bias in
recommender systems with two problematic patterns of data collection, namely observation bias,
which results from feedback loops generated by the system’s recommendations to specific groups
of users, and population imbalance, where the data available to the system reflect existing social
patterns expressing bias towards some groups. They propose a probabilistic programming
approach to mitigate the system’s bias against protected social groups.
On the other hand, (Burke, 2017) suggests to consider fairness in recommendation systems
as a multi-sided concept. Based on this approach, he focuses on three notions of fair
recommendations, taking the perspective of either the user/consumer (C-fairness); or the provider (P-
fairness); or a combination of the two (CP-Fairness). This taxonomy enables the developer of a
recommendation system to identify how the competing interests of different parties are affected
by the system’s recommendations, and hence design system architectures that can mediate
effectively between these interests.
In both approaches, the issue of fairness is tied up with choosing the right LoA for a
specific application of a recommender system. Given that the concept of fairness is strongly tied
to the social context within which the system gathers its data and makes recommendations,
extending the same approach to any application of recommender systems may not be viable.
4.6.!Polarization and social manipulability
A much-discussed effect of some recommender systems is their transformative impact on society.
In particular, news recommender systems and social media filters, by nature of their design, run
the risk of insulating users from exposure to different viewpoints, creating self-reinforcing biases
and “filter bubbles” that are damaging to the normal functioning of public debate, group
deliberation, and democratic institutions more generally (Bozdag, 2013; Bozdag & van den Hoven,
2015; Harambam, Helberger, & van Hoboken, 2018; Helberger, Karppinen, & D’acunto, 2016;
Koene et al., 2015; Reviglio, 2017; Zook et al., 2017). A closely related issue is protecting these
systems from manipulation by (sometimes even small but) especially active groups of users, whose
interactions with the system can generate intense positive feedback, driving up the system’s rate of
recommendations for specific items (Chakraborty, Patro, Ganguly, Gummadi, & Loiseau, 2019).
News recommendation systems, streaming platforms, and social networks can become an arena
for targeted political propaganda, as demonstrated by the recent Cambridge Analytical scandal in
2018, and the documented external interference in US political elections in recent years (Howard,
Ganesh, Liotsiou, Kelly, & François, 2019).
The literature on the topic proposes a range of approaches to increase the diversity of
recommendations. A point noted by several authors is that news recommendation systems, in
particular, must reach a trade-off between the expected relevance to the user and diversity when
generating personalised recommendations based on pre-specified user preferences or behavioural
data (Helberger et al., 2016; Reviglio, 2017). In this respect, (Bozdag & van den Hoven, 2015)
argue that the design of algorithmic tools to combat informational segregation should be more
sensitive to the democratic norms that are implicitly built into these tools.
In general, the approaches to the issue of polarization and social manipulability appear to
be split between bottom-up and top-down strategies, prioritizing either the preferences of users
(and their autonomy in deciding how to configure the personalised recommendations) or the social
preference for a balanced public arena. Once again, some authors take a decidedly user-centred
perspective. For example, (Harambam et al., 2018) propose the use of different “recommendation
personae”, or “pre-configured and anthropomorphised types of recommendation algorithms”
expressing different user preferences with respect to novelty, diversity, relevance, and other
attributes of a recommendation algorithm. In the same vein, (Reviglio, 2017) stresses the
importance of promoting serendipity even at the cost of sacrificing aspects of the user experience,
such as diminished relevance of the recommendations.
Based on the review of the literature presented in the previous section, we can now revisit the
taxonomy that we proposed in Section 3, and place the concerns that we have identified within
the conceptual space that it provides. Table 2 summarises our results.
Table 2
Immediate Harm
Exposure to Risk
Biased recommendations (4.1)
Opacity (4.4)
Questionable content (4.1)
Unfair recommendations (4.5)
Encroachment on individual autonomy
and identity (4.3)
Privacy (4.2)
Social manipulability and Polarisation (4.6)
Starting with privacy, the main challenge that is linked with privacy violations is the possibility of
unfair or otherwise malicious uses of personal data to target individual users. Thus, from our
review, it emerges that privacy concerns may be best conceptualised as exposure to risk. Moreover,
the types of risk to which privacy violations expose users fall mainly under the category of rights
violations, such as unfair targeting and use of manipulative techniques.
Issues of personal autonomy and identity also fall under the category of rights violations, and
constitute cases of immediate violations. Unfair recommendations can be associated with a negative
impact on utility but, as also noted by (Yao & Huang, 2017), fairness and utility are mutually
independent, and unfairness may be best classified as a type of immediate right violation.
A notable insight that emerges from the review is that most of the ethical impacts of
recommender systems identified in the literature are analysed from the perspective of the receivers
of the recommendations. This is evident not only in the reliance on accuracy metrics measuring
the distance between user preferences and recommendations, but also when considering that
privacy, unfairness, opacity, and the appropriateness of content are judged from the perspective
of the individual receiving the recommendations. However, individual users are not the only
stakeholders of recommender systems (Burke, 2017). The utility, rights, and risks carried by
providers of recommender systems, and by society at large, should also be addressed explicitly in the
design and operation of recommender systems. And there are also more complex, nested cases in
which recommendations concern third-parties (e.g., what to buy for a friend’s birthday). Currently,
this is (partially) evident only in the case of discussion on social polarization and its effects on
democratic institutions (reviewed in section 4.6). Failure to address explicitly these additional
perspectives of the ethical impact of recommender systems may lead to masking seriously
problematic practices. A case in point may be that of introducing a “bias” in favour of
recommending unpopular items to maximise catalogue coverage in e-commerce applications
(Jameson et al., 2015). This practice meets a specific need of the provider of a recommendation
system, helping to minimise the number of unsold items, which in this specific instance may be
considered a legitimate interest to be traded off against the utility that a user may receive from a
more accurate recommendation. However, modelling the provider’s interests as a bias added to
the system is unhelpful if the aim is to identify what would be the right level of trade-off between
the provider’s and users’ interests.
Any recommendation is a nudging, and any nudging embeds values. The opacity about
which and whose values are at stake in recommender systems hinders the possibility of designing
better systems that can also promote socially preferable outcomes and improve the balance
between individual and non-individual utilities.
The distribution of the topics by discipline also reveals some interesting insights. Among
the reviewed articles, the ones addressing privacy, fairness and opacity come predominantly from
computer science. This is in line with the general trends in the field of algorithmic approaches to
decision making, and the presence of established metrics and technical approaches to address these
In contrast, the challenges posed by socially transformative effects, manipulability, and
personal autonomy are more difficult to address using purely technical approaches, largely because
their definitions are qualitative, more contentious, and require viewing recommender systems in
the light of the social context in which they operate. Thus, the articles identified in this review that
relate to these issues are much more likely to come from philosophy, anthropology, and science
and technology studies. The methodologies that they adopt are more varied, ranging from
ethnographic study (Seaver, 2018b), to hermeneutics (de Vries, 2010), decision theory (Burr et al.,
2018), and economics (Abdollahpouri et al., 2017).
This article offers a map and an analysis of the main ethical challenges posed by
recommender systems, as identified in the current literature. It also highlights a gap in the relevant
literature, insofar as it stresses the need to consider the interests of providers of recommender
systems, and of society at large (including third-party, nested cases of recommendations), and not
only of the receivers of the recommendation, when assessing the ethical impact of recommender
systems. The next steps are, therefore, filling the gap, and articulating a comprehensive framework
for addressing the ethical challenges posed by recommender systems, based on the taxonomy and
the findings of this review.
6.!Appendix: Methodology
We performed a keyword search on five widely used reference repositories (Google Scholar, IEEE
Xplore, SCOPUS, PhilPapers and ArXiv), using a sting of the general form:
((moral* OR ethic*) AND (recommend* AND (system* OR algorithm*)))
The keyword search produced a total of 533 results, including 417 results on Google Scholar, 54
results on Scopus, 48 results on IEEE Xplore, seven results on PhilPapers, and seven results on
ArXiv. After eliminating duplicate entries, and screening out the irrelevant entries based on the
title and abstract, 50 relevant entries were left. These were reviewed in more detail. Finally,
additional entries were added following the citations in the reviewed articles. The result was a
corpus of 37 relevant works, discussed in this review and listed in the References.
Abdollahpouri, H., Burke, R., & Mobasher, B. (2017). Recommender Systems as Multistakeholder
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a
survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data
Engineering, 17(6), 734749.
Bozdag, E. (2013). Bias in algorithmic filtering and personalization. Ethics and Information Technology,
15, 209–227.
Bozdag, E., & van den Hoven, J. (2015). Breaking the filter bubble: democracy and design. Ethics
and Information Technology, 17(4), 249–265.
Burke, R. (2017). Multisided Fairness for Recommendation.
Burr, C., Cristianini, N., & Ladyman, J. (2018). An Analysis of the Interaction Between Intelligent
Software Agents and Human Users. Minds and Machines, 28(4), 735774.
Chakraborty, A., Patro, G. K., Ganguly, N., Gummadi, K. P., & Loiseau, P. (2019). Equality of
Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations. FATREC.
Chaslot, G. (2018, February 1). How Algorithms Can Learn to Discredit the Media – Guillaume
Chaslot – Medium. Medium.
de Vries, K. (2010). Identity, profiling algorithms and a world of ambient intelligence. Ethics and
Information Technology, 12(1), 71–85.
Farnadi, G., Kouki, P., Thompson, S. K., Srinivasan, S., & Getoor, L. (2018). A Fairness-aware
Hybrid Recommender System. 2nd FATREC Workshop: Responsible Recommendation.
Floridi, L. (2008). Understanding Epistemic Relevance. Erkenntnis, 69(1), 6992.
Floridi, L. (2011). The Construction of Personal Identities Online. Minds and Machines, 21(4), 477
Floridi, L. (2016). The Method of Levels of Abstraction. In L. Floridi (Ed.), The Routledge Handbook
of Philosophy of Information (pp. 67–72). Routledge.
Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A:
Mathematical, Physical and Engineering Sciences, 374(2083), 20160360.
Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016). On the (im)possibility of fairness *.
Friedman, A., Knijnenburg, B., Vanhecke, K., Martens, L., Berkovsky, S., & Berkovsky CSIRO, S.
(2015). Privacy Aspects of Recommender Systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.),
Recommender Systems Handbook (2nd ed., pp. 649688). New York: Springer Science + Business
Germano, F., Gómez, V., & Mens, G. L. (2019). The few-get-richer: a surprising consequence of
popularity-based rankings. ArXiv:1902.02580 [Cs]. Retrieved from
Hansson, S. O. (2010). The Harmful Influence of Decision Theory on Ethics. Ethical Theory and
Moral Practice, 13(5), 585–593.
Harambam, J., Helberger, N., & van Hoboken, J. (2018). Democratizing algorithmic news
recommenders: how to materialize voice in a technologically saturated media ecosystem.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and
Engineering Sciences, 376(2133), 20180088.
Hayenhjelm, M., & Wolff, J. (2012). The Moral Problem of Risk Impositions: A Survey of the
Literature. European Journal of Philosophy, 20, E26–E51.
Helberger, N., Karppinen, K., & D’acunto, L. (2016). Exposure diversity as a design principle for
recommender systems.
Herlocker, J. L., Konstan, J. A., & Riedl, J. (2000). Explaining Collaborative Filtering Recommendations.
Howard, P. N., Ganesh, B., Liotsiou, D., Kelly, J., & François, C. (2019). The IRA, Social Media and
Political Polarization in the United States, 2012-2018.
Jameson, A., Mrtijn c>, W., Felfernig, A., de Gemmis, M., Lops, P., Semeraro, G., & Chen, L.
(2015). Human Decision Making and Recommender Systems. In Francesco Ricci, L. Rokach, &
B. Shapira (Eds.), Recommender Systems Handbook. Springer.
Jannach, D., & Adomavicius, G. (2016). Recommendations with a Purpose. RecSys’16.
Jannach, D., Zanker, M., Ge, M., & Gröning, M. (2012). Recommender Systems in Computer Science and
Information Systems – A Landscape of Research.
Karimi, M., Jannach, D., & Jugovac, M. (2018). News Recommender Systems - Survey and Roads Ahead.
Koene, A., Perez, E., Carter, C. J., Statache, R., Adolphs, S., O’Malley, C., … McAuley, D. (2015).
Ethics of Personalized Information Filtering.
Marlin, B. M., Zemel, R. S., Roweis, S., & Slaney, M. (2007). Collaborative Filtering and the Missing
at Random Assumption. UAI.
Merrill, J. B., & Tobin, A. (2019). Facebook Moves to Block Ad Transparency Tools — Including
Ours. ProPublica.
Narayanan, A. (2008). IEEE Xplore - Robust De-anonymization of Large Sparse Datasets. SP ’08
Proceedings of the 2008 IEEE Symposium on Security and Privacy.
Paraschakis, D. (2016). Recommender Systems from an Industrial and Ethical Perspective.
Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 463466.
Paraschakis, D. (2017). Towards an ethical recommendation framework. 2017 11th International
Conference on Research Challenges in Information Science (RCIS), 211220.
Paraschakis, D. (2018). Algorithmic and Ethical Aspects of Recommender Systems in E-Commerce. Malmö.
Pennock, D. M., Horvitz, E., & Giles, C. L. (2000). Social Choice Theory and Recommender
Systems: Analysis of the Axiomatic Foundations of Collaborative Filtering. AAAI-00.
Reviglio, U. (2017). Serendipity by Design? How to Turn from Diversity Exposure to Diversity Experience to
Face Filter Bubbles in Social Media.
Ricci, Francesco, Rokach, L., & Shapira, B. (Eds.). (2015). Recommender Systems Handbook (2nd ed.).
Retrieved from
Rodriguez, M. A., & Watkins, J. H. (2009). Faith in the Algorithm, Part 2: Computational Eudaemonics.
Seaver, N. (2018a). Captivating algorithms: Recommender systems as traps. Journal of Material
Culture, 135918351882036.
Seaver, N. (2018b). Captivating algorithms: Recommender systems as traps. Journal of Material
Culture, 135918351882036.
Souali, K., El Afia, A., & Faizi, R. (2011). An automatic ethical-based recommender system for e-
commerce. 2011 International Conference on Multimedia Computing and Systems, 14.
Taddeo, M., & Floridi, L. (2018). How AI can be a Force for Good. Science, 361(6404), 751752.
Tang, T. Y., & Winoto, P. (2016). I should not recommend it to you even if you will like it: the
ethics of recommender systems. New Review of Hypermedia and Multimedia, 22(12), 111138.
Tintarev, N., & Masthoff, J. (2011). Designing and Evaluating Explanations for Recommender
Systems. In Recommender Systems Handbook (pp. 479510).
Yao, S., & Huang, B. (2017). Beyond Parity: Fairness Objectives for Collaborative Filtering. NIPS.
Zook, M., Barocas, S., boyd, danah, Crawford, K., Keller, E., Gangadharan, S. P., … Pasquale, F.
(2017). Ten simple rules for responsible big data research. PLOS Computational Biology, 13(3),
ResearchGate has not been able to resolve any citations for this publication.
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