Pre-print of article published 15 February 2019 in Nature Electronics:
Ethical standards in Robotics and AI
A new generation of ethical standards in robotics and artificial intelligence is emerging as a
direct response to a growing awareness of the ethical, legal and societal impact of the fields.
But what exactly are these ethical standards and how do they differ from conventional
Standards are a vital part of the infrastructure of the modern world: invisible, but no
less important than roads, airports and telephone networks. It is hard to think of any
aspect of everyday life untouched by standards. The International Organization for
Standardisation (ISO) – just one of several standards bodies – lists a total of 22,482
published standards. Take the simple act of brushing your teeth in the morning: there
are standards for your toothbrush (both manual ISO 20126 and electric ISO 20127),
your toothpaste and its packaging (ISO 11609), and the quality of your tap water
(ISO 5667-5). Although it might seem odd to wax lyrical on standards, they do
represent a truly remarkable body of work – drafted by countless expert volunteers –
with an extraordinary impact on individual and societal health and safety.
All standards embody a principle and often it is an ethical principle or value. Safety
standards are founded on the general principle that products and systems should do
no harm – that they should be safe; ISO 13482, for instance, sets out safety
requirements for personal care robots. Quality management standards, such as ISO
9001, describe how things should be done, and can be thought of as expressing the
principle that shared best practice leads to improved quality. And technical
standards, like IEEE 802.11 (better known as WiFi), can be thought of as embodying
the benefits of interoperability. Even the basic idea of standards as codifying shared
ways of doing things can be thought of as expressing the values of cooperation and
harmonisation. All standards can therefore be thought of as implicit ethical standards.
We can define an explicit ethical standard as one that addresses clearly articulated
ethical concerns, and seeks – through its application – to, at best remove, hopefully
reduce, or at the very least highlight the potential for unethical impacts or their
What are the ethical principles which underpin these new ethical standards? An
informal survey1 in December 2017 listed a total of ten different sets of ethical
principles for robotics and AI. The earliest (1950) are Asimov’s laws of robotics:
important because they established the principle that robots should be governed by
principles. Very recently we have seen a proliferation of principles; of the ten sets
surveyed seven were published in 2017.
Perhaps not surprisingly these ethical principles have much in common. In summary:
robots and artificial intelligences (AIs) should do no harm, while being free of bias
and deception; respect human rights and freedoms, including dignity and privacy,
while promoting well-being; and be transparent and dependable while ensuring that
the locus of responsibility and accountability remains with their human designers or
operators. Just as interesting is the increasing frequency of their publication: clear
evidence for a growing awareness of the urgent need for ethical principles for
robotics and AI. But, while an important and necessary foundation, principles are not
practice. Ethical standards are the next important step toward ethical governance in
robotics and AI2.
Ethical risk assessment
Almost certainly the world’s first explicit ethical standard in robotics is BS 8611 Guide
to the Ethical Design and Application of Robots and Robotic Systems3, which was
published in April 2016. Incorporating the EPSRC principles of robotics4, BS8611 is
not a code of practice, but instead guidance on how designers can undertake an
ethical risk assessment of their robot or system, and mitigate any ethical risks so
identified. At its heart is a set of 20 distinct ethical hazards and risks, grouped under
four categories: societal, application, commercial & financial, and environmental.
Advice on measures to mitigate the impact of each risk is given, along with
suggestions on how such measures might be verified or validated. The societal
hazards include, for example, loss of trust, deception, infringements of privacy and
confidentiality, addiction, and loss of employment. The idea of ethical risk
assessment is of course not new – it is essentially what research ethics committees
do – but a method for assessing robots for ethical risks is a powerful new addition to
the ethical roboticist’s toolkit.
In April 2016, the IEEE Standards Association launched a global initiative on the
Ethics of Autonomous and Intelligent Systems5. The significance of this initiative
cannot be overstated; coming from a professional body with the standing and reach
of the IEEE Standards Association it marks a watershed in the emergence of ethical
standards. And it is a radical step. As I’ve argued above all standards are – even if
not explicitly – based on ethical principles. But for a respected standards body to
launch an initiative which explicitly aims to address the deep ethical challenges that
face the whole of autonomous and intelligent systems – from driverless car autopilots
to medical diagnosis AIs, drones to deep learning, and care robots to chat bots – is
both ambitious and unprecedented.
The IEEE initiative positions human well-being as its central tenet6. This is a bold and
political stance since it explicitly seeks to reposition robotics and AI as technologies
for improving the human condition rather than simply vehicles for economic growth.
The initiative’s mission is “to ensure every stakeholder involved in the design and
development of autonomous and intelligent systems is educated, trained, and
empowered to prioritize ethical considerations so that these technologies are
advanced for the benefit of humanity”.
The first major output from the IEEE Standards Association’s global ethics initiative is
a discussion document called Ethically Aligned Design (EAD)7, developed through an
iterative process which invited public feedback. The published second edition of EAD
sets out more than 100 ethical issues and recommendations, and a third edition will
be launched early in 2019. The work of more than 1000 volunteers across thirteen
committees, EAD covers: general (ethical) principles; how to embed values into
autonomous intelligent systems; methods to guide ethical design; safety and
beneficence of artificial general intelligence and artificial superintelligence; personal
data and individual access control; reframing autonomous weapons systems;
economics and humanitarian issues; law; affective computing; classical ethics in AI;
policy; mixed-reality, and well-being.
Each EAD committee was additionally tasked with identifying, recommending and
promoting new candidate standards, and – to date – a total of 14 new IEEE
standards working groups have started work on drafting so called human standards
// start Box 1
Box 1: IEEE P7000 series human standards in development
P7000 – Model Process for Addressing Ethical Concerns During System Design
P7001 – Transparency of Autonomous Systems
P7002 – Data Privacy Process
P7003 – Algorithmic Bias Considerations
P7004 – Standard for Child and Student Data Governance
P7005 – Standard for Transparent Employer Data Governance
P7006 – Standard for Personal Data Artificial Intelligence (AI) Agent
P7007 – Ontological Standard for Ethically Driven Robotics and Automation Systems
P7008 – Standard for Ethically Driven Nudging for Robotic, Intelligent and
P7009 – Standard for Fail-Safe Design of Autonomous and Semi-Autonomous
P7010 – Wellbeing Metrics Standard for Ethical Artificial Intelligence and
P7011 – Standard for the Process of Identifying and Rating the Trustworthiness of
P7012 – Standard for Machine Readable Personal Privacy Terms
P7013 – Inclusion and Application Standards for Automated Facial Analysis
// end Box 1
The importance of transparency and explainability
Consider P7001 as a case study. One of the general principles8 of EAD asks “how
can we ensure that autonomous and intelligent systems are transparent?”, and
recommends a new standard for transparency. P7001 Transparency in Autonomous
Systems was initiated as a direct response. IEEE P7001 directly addresses the
straightforward ethical principle that it should always be possible to find out why an
autonomous system made a particular decision.
A robot or AI is transparent if it is possible to find out why it behaves in a certain way.
We might for instance want to discover why it made a particular decision, especially if
that decision caused an accident – or for the less serious reason that the robot or
AI’s behaviour is puzzling. Transparency is not intrinsic to robots and AIs, but must
be designed for, and it is a property which autonomous systems might have more or
less of. And full transparency might be very challenging to provide, for instance in
systems based on artificial neural networks (deep learning systems), or systems that
are continually learning.
There are two reasons transparency is so important.
First, because modern robots and AIs are designed to work with or alongside
humans, who need to be able to understand what they are doing and why. If we take
an assisted living robot as an example transparency (or to be precise, explainability)
means the user can understand what the robot might do in different circumstances.
An elderly person might be very unsure about robots, so it is important that her robot
is helpful, predictable – never does anything that frightens her – and above all safe. It
should be easy for her to learn what the robot does and why, in different
circumstances. An explainer system that allows her to ask the robot “why did you just
do that?” and receive a simple natural language explanation would be very helpful in
providing this kind of transparency. A higher level of transparency would be the
ability to ask questions like “what would you do if I fell down?” or “what would you do
if I forget to take my medicine?” This allows her to build a mental model of how the
robot will behave in different situations.
And second, because robots and AIs can and do go wrong. If physical robots go
wrong they can cause physical harm or injury. Real world trials of driverless cars
have already resulted in several fatalities9. Even a software AI can cause harm. A
medical diagnosis AI might, for instance, give the wrong diagnosis, or a biased credit
scoring AI might cause someone’s loan application to be wrongly rejected. Without
transparency, discovering what went wrong is extremely difficult and may – in some
cases – be impossible. The ability to find out what went wrong and why is not only
important to accident investigators, it might also be important to establish who is
responsible, for insurance purposes, or in a court of law. And following high profile
accidents wider society needs the reassurance of knowing that problems have been
found and fixed.
Transparency and explainability measured
But transparency is not one thing. Clearly an elderly relative does not require the
same level of understanding of a care robot as the engineer who repairs it. The
P7001 working group has defined five distinct groups of stakeholders (the
beneficiaries of the standard): users, safety certifiers or agencies, accident
investigators, lawyers or expert witness, and the wider public. For each of these
stakeholder groups, P7001 is setting out measurable, testable levels of transparency
so that autonomous systems can be objectively assessed and levels of compliance
determined, in a range that defines minimum levels up to the highest achievable
standards of transparency.
Of course, the way in which transparency is provided is very different for each group.
Safety certification agencies need access to technical details of how the system
works, together with verified test results. Accident investigators will need access to
data logs of exactly what happened prior to and during an accident, most likely
provided by something akin to an aircraft flight data recorder10. Lawyers and expert
witnesses will need access to the reports of safety certifiers and accident
investigators, along with evidence of the developer or manufacturer’s quality
management processes. And wider society needs accessible documentary-type
science communication to explain autonomous systems and how they work. P7001
will provide system designers with a toolkit for self-assessing transparency, and
recommendations for how to achieve greater transparency and explainability.
How might these new ethical standards be applied when, like most standards, they
are voluntary? First, standards which relate to safety (and especially safety-critical
systems), can be mandated by licensing authorities, so that compliance with those
standards becomes a de facto requirement of obtaining a licence to operate that
system; for the P7000 series candidates might include P7001 and P7009. Second, in
a competitive market, compliance with ethical standards can be used to gain market
advantage – especially among ethically aware consumers. Third, there is growing
pressure from professional bodies for their members to behave ethically. Emerging
professional codes of ethical conduct such as the recently published ACM11 and
IEEE12 codes of ethics and professional conduct are very encouraging; in turn, those
professionals are increasingly likely to exert internal pressure on their employers to
adopt ethical standards. And fourth, soft governance plays an important role in the
adoption of new standards: by requiring compliance with standards as a condition of
awarding procurement contracts governments can and do influence and direct the
adoption of standards – across an entire supply chain – without explicit regulation.
For data- or privacy-critical applications, a number of the P7000 standards
(P7002/3/4/5/12 and 13, for instance) could find application this way.
While some argue over the pace and level of impact of robotics and AI (on jobs, for
instance), most agree that increasingly capable intelligent systems create significant
ethical challenges, as well as great promise. This new generation of ethical
standards takes a powerful first step toward addressing those challenges. Standards,
like open science13, are a trust technology. Without ethical standards, it is hard to see
how robots and AIs will be trusted and widely accepted, and without that acceptance
their great promise will not be realised.
Alan Winfield is Professor of Robot Ethics at the Bristol Robotics Laboratory, UWE
Bristol, and visiting professor at the University of York. He chairs IEEE Standards
Working Group P7001.
The views expressed in this article are those of the author only, and do not represent
the opinions of any organisation mentioned, or with which I am affiliated.
2. Winfield, A. F. & Jirotka, M. Phil. Trans. R. Soc. A 376, 20180085 (2018);
3. British Standards Institute, BS 8611:2016 Robots and robotic devices. Guide to
the ethical design and application of robots and robotic systems (2016)
4. EPSRC, Principles of Robotics (2011)
7. IEEE Standards Association, Ethically Aligned Design (2017)
9. Stilgoe J, Winfield A, The Guardian, 13 April 2018
10. Winfield A.F., Jirotka M. (2017) The Case for an Ethical Black Box. In: Gao Y.,
Fallah S., Jin Y., Lekakou C. (eds) Lecture Notes in Computer Science, vol
10454. Springer, Cham.
13. Grand, A., Wilkinson, C., Bultitude, K. & Winfield, A.F. Open Science: A new
‘trust technology’? Science Communication 34, 679- 689 (2012).