On Sustainable Robotics

The climate emergency brooks no compromise: every human activity or artefact is either part of the solution or it is part of the problem. 

I've worried about the sustainability of consumer electronics for some time, and, more recently, the shocking energy costs of big AI. But the climate emergency has also caused me to think hard about the sustainability of robots. In recent papers we have defined responsible robotics as

... the application of Responsible Innovation in the design, manufacture, operation, repair and end-of-life recycling of robots, that seeks the most benefit to society and the least harm to the environment.

I will wager that few robotics manufacturers - even the most responsible - pay much attention to repairability and environmental impact. And, I'm ashamed to say, very little robotics research is focused on the development of sustainable robots. A search on google scholar throws up just a handful of great papers detailing work on upcycled and sustainable robots (2018), sustainable robotics for smart cities (2018), and sustainable soft robots (2020).

I was then delighted when, a few weeks ago, my friend and colleague Michael Fisher, drafted a proposal for a new standard on Sustainable Robotics. The proposal received strong support from the BSI robotics committee. Here is the formal notice requesting comments on Michael's proposal: BS XXXX Guide to the Sustainable Design and Application of Robotic Systems. Anyone can comment (although you do need to register first). The deadline is 1 April 2021. 

So what would make a robot sustainable? In my view it would have to be:

1. Made from sustainable materials. This means the robot should, as far as possible, use recycled materials (plastics or metals), or biodegradable materials like wood. Any new materials should be ethically sourced. 
2. Low energy. The robot should be designed to use as little energy as possible. It should have energy saving modes. If an outdoor robot then it should use solar cells and/or hydrogen cells when they become small enough for mobile robots. Battery powered robots should always be rechargeable. 
3. Repairable. The robot would be designed for ease of repair, using modular, replaceable parts as much as possible - especially the battery. Additionally the manufacturers should provide a repair manual so that local workshops could fix most faults. 
4. Recyclable. Robots will eventually come to the end of their useful life, and if they cannot be repaired or recycled we risk them being dumped in landfill. To reduce this risk the robot should be designed to make it easy to re-use parts, such as electronics and motors, and re-cycle batteries, metals and plastics.
   

These are, for me, the four fundamental requirements, but there are others. The BSI proposal adds the environmental effects of deployment (it is unlikely we would consider a sustainable robot designed to spray pesticides as truly sustainable), or of failure in the field. Also the environmental effect of maintenance; cleaning materials, for instance. The proposal also looks toward sustainable, upcyclable robots as part of a circular economy.

This is Ecobot III, developed some years ago by colleagues in the Bristol Robotics Lab's Bio-energy group. The robot runs on electricity extracted from biomass by 48 microbial fuel cells (the two concentric brick coloured rings). The robot is 90% 3D printed, and the plastic is recyclable.

 

 

 

 

 

I would love to see, in the near term, not only a new standard on Sustainable Robotics as a guide (and spur) for manufacturers, but the emergence of Sustainable Robotics as a thriving new sub-discipline in robotics.

New IET online course on Robot Ethics goes live

Big day today. My online course on Robot Ethics has been launched on the Institution of Engineering and Technology (IET) Academy web pages. The aim of the course is to give a comprehensive introduction to robot ethics and responsible robotics, and machine ethics. As well as ethical principles the course introduces powerful practical tools including Ethically Aligned Design (also called values driven design), emerging new ethical standards including BS8611 and the powerful method Ethical Risk Assessment,  IEEE 7001 on Transparency, and equally essential Ethical Governance, while showing how ethics, standards and regulation are linked. The course took the best part of 18 months to write, not least because of the strict formatting and style required for IET online courses. For academics, writing courses normally means just creating slides, but - to my surprise - IET online courses are narrated by professional voice actors, so I had to write the narration for each slide. Plus, alot of tests to help students to self-test their understanding.

The course is organized as 10 one hour units, each with several modules, and tests at the end of each module and at the end of the unit. Here is the outline syllabus.

Unit 1: What is Robot Ethics?

This unit defines what we mean by an intelligent robot, robot ethics and ethical robots.

Module 1: Defines what we mean by a robot and robot autonomy, while explaining the difference between first wave (i.e. industrial) robots and second wave (i.e. social) robots
Module 2: Defines intelligence and clarifies the distinction between robotics and Artificial Intelligence (AI)
Module 3: Robot/AI ethics: ethics for humans and responsible robotics
Module 4: Machine ethics: ethics for robots

Unit 2: Inspired by Asimov – The EPSRC Principles of Robotics

This unit focuses on the influential EPSRC Principles of Robots.

Module 1: Asimov’s Three Laws of Robotics, their limitations, and their contribution to robot ethics
Module 2: Why robot ethics are so important today
Module 3: The EPSRC Principles of Robotics
Module 4: Responsible Robotics

Unit 3: An Overview of Ethical Frameworks for AI

This unit looks at some of the more recent ethical frameworks proposed for robotics and AI.

Module 1: A Proliferation of Principles. A helicopter view of all of the ethical frameworks for robotics and artificial intelligence published since Asimov’s laws of robotics. Including what an ethical framework is and what it does and does not offer.
Module 2: The Future of Life Institute Asilomar principles for beneficial AI
Module 3: The UNI Global Union Top 10 Principles for Ethical AI
Module 4: The European Commission’s High Level Expert Group on AI Ethics Guidelines for Trustworthy AI

Module 5: The OECD Principles of AI

Module 6: Summary: comparing ethical frameworks and their limitations 

Unit 4: Ethical Standards in Robotics

This unit explores emerging ethical standards.

Module 1: Standards, an introduction
Module 2: An Ethical Standard - British Standard BS8611:2016 A Guide to the Ethical Design of Robots and Robotic Systems
Module 3: Ethical Risk Assessment based on BS8611, including a Case Study
Module 4: Standards in Practice

Unit 5: Ethically Aligned Design in Robotics and AI

This unit introduces the IEEE global ethics initiative and ethically aligned design.

Module 1: The IEEE Global Ethics Initiative
Module 2: The IEEE General Principles
Module 3: Ethically Aligned Design
Module 4: The P70XX Human Standards

Unit 6: Transparency and Explainability in Robotics and AI

This Unit explores transparency, and the related topic of accident investigation.

Module 1: Introduction to Transparency and Explainability
Module 2: The IEEE P7001 Standard on Transparency in Autonomous Systems
Module 3: Robot Accident Investigation, an introduction

Unit 7: Ethical Governance for Robotics

This unit focuses on ethical governance for robotics.

Module 1: How do we trust our technology?
Module 2: A Roboethics Roadmap, linking ethics, standards and regulation
Module 3: Robotics Law and Regulation, with examples from Drones, Autonomous Vehicles and Assisted Living robots
Module 4: A framework for ethical governance

Unit 8: Machine Ethics 1 – An Asimovian Ethical Robot

In this unit, we will explore machine ethics, and ask the question: is it possible to build a moral machine?

Module 1: A thought experiment: is it possible to build a moral machine?
Module 2: The Consequence Engine
Module 3: Experimental trials of an Asimovian ethical robot

Unit 9: Machine Ethics 2 – Approaches, Risks and Governance

Module 1: Categories of ethical agency
Module 2: Approaches to building ethical robots
Module 3: The risks of ethical robots
Module 4: The governance of ethical machines

Unit 10: Final Assessment

 

RoboTED: a case study in Ethical Risk Assessment

A few weeks ago I gave a short paper* at the excellent International Conference on Robot Ethics and Standards (ICRES 2020), outlining a case study in Ethical Risk Assessment - see our paper here. Our chosen case study is a robot teddy bear, inspired by one of my favourite movie robots: Teddy, in A.I. Artificial Intelligence.

Although Ethical Risk Assessment (ERA) is not new - it is after all what research ethics committees do - the idea of extending traditional risk assessment, as practised by safety engineers, to cover ethical risks is new. ERA is I believe one of the most powerful tools available to the responsible roboticist, and happily we already have a published standard setting out a guideline on ERA for robotics in BS 8611, published in 2016.

Before looking at the ERA, we need to summarise the specification of our fictional robot teddy bear: RoboTed. First, RoboTed is based on the following technology:

• RoboTed is an Internet (WiFi) connected device, 
• RoboTed has cloud-based speech recognition and conversational AI (chatbot) and local speech synthesis,
• RoboTed’s eyes are functional cameras allowing RoboTed to recognise faces,
• RoboTed has motorised arms and legs to provide it with limited baby-like movement and locomotion.

And second RoboTed is designed to:

• Recognise its owner, learning their face and name and turning its face toward the child.
• Respond to physical play such as hugs and tickles.
• Tell stories, while allowing a child to interrupt the story to ask questions or ask for sections to be repeated.
• Sing songs, while encouraging the child to sing along and learn the song.
• Act as a child minder, allowing parents to both remotely listen, watch and speak via RoboTed.

The tables below summarise the ERA of RoboTED for (1) psychological, (2) privacy & transparency and (3) environmental risks. Each table has 4 columns, for the hazard, risk, level of risk (high, medium or low) and actions to mitigate the risk. BS8611 defines an ethical risk as the “probability of ethical harm occurring from the frequency and severity of exposure to a hazard”; an ethical hazard as “a potential source of ethical harm”, and an ethical harm as “anything likely to compromise psychological and/or societal and environmental well-being".

(1) Psychological Risks

(2) Security and Transparency Risks

(3) Environmental Risks

For a more detailed commentary on each of these tables see our full paper - which also, for completeness, covers physical (safety) risks.

And here are the slides from my short ICRES 2020 presentation:

Through this fictional case study we argue we have demonstrated the value of ethical risk assessment. Our RoboTed ERA has shown that attention to ethical risks can

• suggest new functions, such as “RoboTed needs to sleep now”,
• draw attention to how designs can be modified to mitigate some risks, 
• highlight the need for user engagement, and
• reject some product functionality as too risky.

But ERA is not guaranteed to expose all ethical risks. It is a subjective process which will only be successful if the risk assessment team are prepared to think both critically and creatively about the question: what could go wrong? As Shannon Vallor and her colleagues write in their excellent Ethics in Tech Practice toolkit design teams must develop the “habit of exercising the skill of moral imagination to see how an ethical failure of the project might easily happen, and to understand the preventable causes so that they can be mitigated or avoided”.

 

*Which won the conference best paper prize!

Back to robot coding part 1: hello world

One of the many things I promised myself when I retired nearly two years ago was to get back to some coding. Why? Two reasons: one is that writing and debugging code is hugely satisfying - for those like me not smart enough to do pure maths or theoretical physics - it's the closest you can get to working with pure mind stuff. But the second is that I want to prototype a number of ideas in cognitive robots which tie together work in artificial theory of mind and the ethical black box, with old ideas on how robots telling each other stories and new ideas on how social robots might be able to explain themselves in response to questions like "Robot: what would you do if I forget to take my medicine?"

But before starting to work on the robot (a NAO) I first needed to learn Python, so completed most of the Codecadamy's excellent Learn Python 2 course over the last few weeks. I have to admit that I started learning Python with big misgivings over the language. I especially don't like the way Python plays fast and loose with variable types, allowing you to arbitrarily assign a thing (integer, float, string, etc) to a variable and then assign a different kind of thing to the same variable; very different to the strongly typed languages I have used since student days: Algol 60, Algol 68, Pascal and C. However, there are things I do like: the use of indentation as part of the syntax for instance, and lots of nice built in functions like range(), so x = range(0,10) puts a list ('array' in old money) of integers from 0 to 9 in x. 

So, having got my head around Python I finally made a start with the robot on Thursday last week. I didn't get far and it was *very* frustrating. 

Act 1: setting up on my Mac

Attempting to set things up on my elderly Mac air was a bad mistake which sent me spiralling down a rabbit hole of problems. The first thing you have to do is download and unzip the NAO API, called naoqi, from Aldebaran. The same web page then suggests you simply try to import naoqi from within Python, and if there are no errors all's well.

As soon as I got the export path commands right,  import naoqi resulted in the following error

...

Reason: unsafe use of relative rpath libboost_python.dylib in /Users/alansair/Desktop/naoqi/pynaoqi-python2.7-2.1.4.13-mac64/_qi.so with restricted binary

According to stack overflow this problem is caused by Mac OSX system integrity protection (SIP). 

Then (somewhat nervously) I tried turning SIP off, as instructed here.

But import naoqi still gives a different error. Perhaps its because my Python is in the wrong place, the Aldebaran page says it must be at /usr/local/bin/python (the default on the mac is /usr/bin. Ok so I So, reinstall python 2.7 from Python.org  so that it is in /usr/local/bin/python. But now I get another error message:

>> import naoqi

Fatal Python error: PyThreadState_Get: no current thread

Abort trap: 6

A quick search and I read: "this error shows up when a module tries to use a python library that is different than the one the interpreter uses, that is, when you mix two different pythons. I would run otool -L <dyld> on each of the dynamic libraries in the list of Binary Images, and see which ones is linked to the system Python."

At which point I admitted defeat.

Act 2: setting up on my Linux machine

Once I had established that the Python on my Linux machine was also the required version 2.7, I then downloaded and unzipped the NAO API, this time for Linux.

This time I was able to import naoqi with no errors, and within just a few minutes ran my first NAO program: hello world. 

from naoqi import ALProxy

tts = ALProxy("ALTextToSpeech", "164.168.0.17", 9559)

tts.say("Hello, world!")

whereupon my NAO robot spoke the words "Hello world". Success!

Ethical Standards in Robotics and AI: what they are and why they matter

Here are the slides for my keynote, presented this morning at the International Conference on Robot Ethics and Standards (ICRES 2019). The talk is based on my paper Ethical Standards in Robotics and AI published in Nature Electronics a few months ago (here is a pre-print).



To see the speaker notes click on the options button on the google slides toolbar above.

An Updated Round Up of Ethical Principles of Robotics and AI

This blogpost is an updated round up of the various sets of ethical principles of robotics and AI that have been proposed to date, ordered by date of first publication.

I previously listed principles published before December 2017 here; this blogpost appends those principles drafted since January 2018 (plus one in October 2017 I had missed). The principles are listed here (in full or abridged) with links, notes and references but without critique.

Scroll down to the next horizontal line for the updates.

If there any (prominent) ones I’ve missed please let me know.

Asimov’s three laws of Robotics (1950)

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

I have included these to explicitly acknowledge, firstly, that Asimov undoubtedly established the principle that robots (and by extension AIs) should be governed by principles, and secondly that many subsequent principles have been drafted as a direct response. The three laws first appeared in Asimov’s short story Runaround [1]. This wikipedia article provides a very good account of the three laws and their many (fictional) extensions.

Murphy and Wood’s three laws of Responsible Robotics (2009)

1. A human may not deploy a robot without the human-robot work system meeting the highest legal and professional standards of safety and ethics. 
2. A robot must respond to humans as appropriate for their roles. 
3. A robot must be endowed with sufficient situated autonomy to protect its own existence as long as such protection provides smooth transfer of control which does not conflict with the First and Second Laws. 

These were proposed in Robin Murphy and David Wood’s paper Beyond Asimov: The Three Laws of Responsible Robotics [2].

EPSRC Principles of Robotics (2010) 

1. Robots are multi-use tools. Robots should not be designed solely or primarily to kill or harm humans, except in the interests of national security.
2. Humans, not Robots, are responsible agents. Robots should be designed and operated as far as practicable to comply with existing laws, fundamental rights and freedoms, including privacy.
3. Robots are products. They should be designed using processes which assure their safety and security.
4. Robots are manufactured artefacts. They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent.
5. The person with legal responsibility for a robot should be attributed.

These principles were drafted in 2010 and published online in 2011, but not formally published until 2017 [3] as part of a two-part special issue of Connection Science on the principles, edited by Tony Prescott & Michael Szollosy [4]. An accessible introduction to the EPSRC principles was published in New Scientist in 2011.

Future of Life Institute Asilomar principles for beneficial AI (Jan 2017)

I will not list all 23 principles but extract just a few to compare and contrast with the others listed here:

6. Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.
7. Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.
8. Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
9. Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.
10. Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.

11. Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.
12. Personal Privacy: People should have the right to access, manage and control the data they generate, given AI systems’ power to analyze and utilize that data.
13. Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.
14. Shared Benefit: AI technologies should benefit and empower as many people as possible.
15. Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.

An account of the development of the Asilomar principles can be found here.

The ACM US Public Policy Council Principles for Algorithmic Transparency and Accountability (Jan 2017)

1. Awareness: Owners, designers, builders, users, and other stakeholders of analytic systems should be aware of the possible biases involved in their design, implementation, and use and the potential harm that biases can cause to individuals and society.
2. Access and redress: Regulators should encourage the adoption of mechanisms that enable questioning and redress for individuals and groups that are adversely affected by algorithmically informed decisions.
3. Accountability: Institutions should be held responsible for decisions made by the algorithms that they use, even if it is not feasible to explain in detail how the algorithms produce their results.
4. Explanation: Systems and institutions that use algorithmic decision-making are encouraged to produce explanations regarding both the procedures followed by the algorithm and the specific decisions that are made. This is particularly important in public policy contexts.
5. Data Provenance: A description of the way in which the training data was collected should be maintained by the builders of the algorithms, accompanied by an exploration of the potential biases induced by the human or algorithmic data-gathering process.
6. Auditability: Models, algorithms, data, and decisions should be recorded so that they can be audited in cases where harm is suspected.
7. Validation and Testing: Institutions should use rigorous methods to validate their models and document those methods and results.

See the ACM announcement of these principles here. The principles form part of the ACM’s updated code of ethics.

Japanese Society for Artificial Intelligence (JSAI) Ethical Guidelines (Feb 2017)

1. Contribution to humanity Members of the JSAI will contribute to the peace, safety, welfare, and public interest of humanity.
2. Abidance of laws and regulations Members of the JSAI must respect laws and regulations relating to research and development, intellectual property, as well as any other relevant contractual agreements. Members of the JSAI must not use AI with the intention of harming others, be it directly or indirectly.
3. Respect for the privacy of others Members of the JSAI will respect the privacy of others with regards to their research and development of AI. Members of the JSAI have the duty to treat personal information appropriately and in accordance with relevant laws and regulations.
4. Fairness Members of the JSAI will always be fair. Members of the JSAI will acknowledge that the use of AI may bring about additional inequality and discrimination in society which did not exist before, and will not be biased when developing AI.
5. Security As specialists, members of the JSAI shall recognize the need for AI to be safe and acknowledge their responsibility in keeping AI under control.
6. Act with integrity Members of the JSAI are to acknowledge the significant impact which AI can have on society.
7. Accountability and Social Responsibility Members of the JSAI must verify the performance and resulting impact of AI technologies they have researched and developed.
8. Communication with society and self-development Members of the JSAI must aim to improve and enhance society’s understanding of AI.
9. Abidance of ethics guidelines by AI AI must abide by the policies described above in the same manner as the members of the JSAI in order to become a member or a quasi-member of society.

An explanation of the background and aims of these ethical guidelines can be found here, together with a link to the full principles (which are shown abridged above).

Draft principles of The Future Society’s Science, Law and Society Initiative (Oct 2017)

1. AI should advance the well-being of humanity, its societies, and its natural environment.
2. AI should be transparent.
3. Manufacturers and operators of AI should be accountable.
4. AI’s effectiveness should be measurable in the real-world applications for which it is intended.
5. Operators of AI systems should have appropriate competencies.
6. The norms of delegation of decisions to AI systems should be codified through thoughtful, inclusive dialogue with civil society.

This article by Nicolas Economou explains the 6 principles with a full commentary on each one.

Montréal Declaration for Responsible AI draft principles (Nov 2017)

1. Well-being The development of AI should ultimately promote the well-being of all sentient creatures.
2. Autonomy The development of AI should promote the autonomy of all human beings and control, in a responsible way, the autonomy of computer systems.
3. Justice The development of AI should promote justice and seek to eliminate all types of discrimination, notably those linked to gender, age, mental / physical abilities, sexual orientation, ethnic/social origins and religious beliefs.
4. Privacy The development of AI should offer guarantees respecting personal privacy and allowing people who use it to access their personal data as well as the kinds of information that any algorithm might use.
5. Knowledge The development of AI should promote critical thinking and protect us from propaganda and manipulation.
6. Democracy The development of AI should promote informed participation in public life, cooperation and democratic debate.
7. Responsibility The various players in the development of AI should assume their responsibility by working against the risks arising from their technological innovations.

The Montréal Declaration for Responsible AI proposes the 7 values and draft principles above (here in full with preamble, questions and definitions).

IEEE General Principles of Ethical Autonomous and Intelligent Systems (Dec 2017)

1. How can we ensure that A/IS do not infringe human rights?
2. Traditional metrics of prosperity do not take into account the full effect of A/IS technologies on human well-being.
3. How can we assure that designers, manufacturers, owners and operators of A/IS are responsible and accountable?
4. How can we ensure that A/IS are transparent?
5. How can we extend the benefits and minimize the risks of AI/AS technology being misused?

These 5 general principles appear in Ethically Aligned Design v2, a discussion document drafted and published by the IEEE Standards Association Global Initiative on Ethics of Autonomous and Intelligent Systems. The principles are expressed not as rules but instead as questions, or concerns, together with background and candidate recommendations.

A short article co-authored with IEEE general principles co-chair Mark Halverson Why Principles Matter explains the link between principles and standards, together with further commentary and references.

Note that these principles have been revised and extended, in March 2019 (see below).

UNI Global Union Top 10 Principles for Ethical AI (Dec 2017)

1. Demand That AI Systems Are Transparent
2. Equip AI Systems With an Ethical Black Box
3. Make AI Serve People and Planet
4. Adopt a Human-In-Command Approach
5. Ensure a Genderless, Unbiased AI
6. Share the Benefits of AI Systems
7. Secure a Just Transition and Ensuring Support for Fundamental Freedoms and Rights
8. Establish Global Governance Mechanisms
9. Ban the Attribution of Responsibility to Robots
10. Ban AI Arms Race

Drafted by UNI Global Union‘s Future World of Work these 10 principles for Ethical AI (set out here with full commentary) “provide unions, shop stewards and workers with a set of concrete demands to the transparency, and application of AI”.

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Updated principles

Intel’s recommendation for Public Policy Principles on AI (October 2017)

1. Foster Innovation and Open Development – To better understand the impact of AI and explore the broad diversity of AI implementations, public policy should encourage investment in AI R&D. Governments should support the controlled testing of AI systems to help industry, academia, and other stakeholders improve the technology.
2. Create New Human Employment Opportunities and Protect People’s Welfare – AI will change the way people work. Public policy in support of adding skills to the workforce and promoting employment across different sectors should enhance employment opportunities while also protecting people’s welfare.
3. Liberate Data Responsibly – AI is powered by access to data. Machine learning algorithms improve by analyzing more data over time; data access is imperative to achieve more enhanced AI model development and training. Removing barriers to the access of data will help machine learning and deep learning reach their full potential.
4. Rethink Privacy – Privacy approaches like The Fair Information Practice Principles and Privacy by Design have withstood the test of time and the evolution of new technology. But with innovation, we have had to “rethink” how we apply these models to new technology.
5. Require Accountability for Ethical Design and Implementation – The social implications of computing have grown and will continue to expand as more people have access to implementations of AI. Public policy should work to identify and mitigate discrimination caused by the use of AI and encourage designing in protections against these harms.

These principles were announced in a blog post by Naveen Rao (Intel VP AI) here.

Lords Select Committee 5 core principles to keep AI ethical (April 2018)

1. Artificial intelligence should be developed for the common good and benefit of humanity.
2. Artificial intelligence should operate on principles of intelligibility and fairness.
3. Artificial intelligence should not be used to diminish the data rights or privacy of individuals, families or communities.
4. All citizens have the right to be educated to enable them to flourish mentally, emotionally and economically alongside artificial intelligence.
5. The autonomous power to hurt, destroy or deceive human beings should never be vested in artificial intelligence.

These principles appear in the UK House of Lords Select Committee on Artificial Intelligence report AI in the UK: ready, willing and able? published in April 2019. The WEF published a summary and commentary here.

AI UX: 7 Principles of Designing Good AI Products (April 2018)

1. Differentiate AI content visually – let people know if an algorithm has generated a piece of content so they can decide for themselves whether to trust it or not.
2. Explain how machines think – helping people understand how machines work so they can use them better
3. Set the right expectations – especially in a world full of sensational, superficial news about new AI technologies.
4. Find and handle weird edge cases – spend more time testing and finding weird, funny, or even disturbing or unpleasant edge cases.
5. User testing for AI products (default methods won’t work here).
6. Provide an opportunity to give feedback.

These principles, focussed on the design of the User Interface (UI) and User Experience (UX), are from Budapest based company UX Studio.

The Toronto Declaration on equality and non-discrimination in machine learning systems (May 2018)The Toronto Declaration: Protecting the right to equality and non-discrimination in machine learning systems does not succinctly articulate ethical principles but instead presents arguments under the following headings to address concerns “about the capability of [machine learning] systems to facilitate intentional or inadvertent discrimination against certain individuals or groups of people”.

1. Using the framework of international human rights law The right to equality and non-discrimination; Preventing discrimination, and Protecting the rights of all individuals and groups: promoting diversity and inclusion
2. Duties of states: human rights obligations State use of machine learning systems; Promoting equality, and Holding private sector actors to account
3. Responsibilities of private sector actors human rights due diligence
4. The right to an effective remedy

Google AI Principles (June 2018)

1. Be socially beneficial.
2. Avoid creating or reinforcing unfair bias.
3. Be built and tested for safety.
4. Be accountable to people.
5. Incorporate privacy design principles.
6. Uphold high standards of scientific excellence.
7. Be made available for uses that accord with these principles.

These principles were launched with a blog post and commentary by Google CEO Sundar Pichai here.

IBM’s 5 ethical AI principles (September 2018)

1. Accountability: AI designers and developers are responsible for considering AI design, development, decision processes, and outcomes.
2. Value alignment: AI should be designed to align with the norms and values of your user group in mind.
3. Explainability: AI should be designed for humans to easily perceive, detect, and understand its decision process, and the predictions/recommendations. This is also, at times, referred to as interpretability of AI. Simply speaking, users have all rights to ask the details on the predictions made by AI models such as which features contributed to the predictions by what extent. Each of the predictions made by AI models should be able to be reviewed.
4. Fairness: AI must be designed to minimize bias and promote inclusive representation.
5. User data rights: AI must be designed to protect user data and preserve the user’s power over access and uses

For a full account read IBM’s Everyday Ethics for Artificial Intelligence here.

Microsoft Responsible bots: 10 guidelines for developers of conversational AI (November 2018)

1. Articulate the purpose of your bot and take special care if your bot will support consequential use cases.
2. Be transparent about the fact that you use bots as part of your product or service.
3. Ensure a seamless hand-off to a human where the human-bot exchange leads to interactions that exceed the bot’s competence.
4. Design your bot so that it respects relevant cultural norms and guards against misuse.
5. Ensure your bot is reliable.
6. Ensure your bot treats people fairly.
7. Ensure your bot respects user privacy.
8. Ensure your bot handles data securely.
9. Ensure your bot is accessible.
10. Accept responsibility.

Microsoft’s guidelines for the ethical design of ‘bots’ (chatbots or conversational AIs) are fully described here.

CEPEJ European Ethical Charter on the use of artificial intelligence (AI) in judicial systems and their environment, 5 principles (February 2019)

1. Principle of respect of fundamental rights: ensuring that the design and implementation of artificial intelligence tools and services are compatible with fundamental rights.
2. Principle of non-discrimination: specifically preventing the development or intensification of any discrimination between individuals or groups of individuals.
3. Principle of quality and security: with regard to the processing of judicial decisions and data, using certified sources and intangible data with models conceived in a multi-disciplinary manner, in a secure technological environment.
4. Principle of transparency, impartiality and fairness: making data processing methods accessible and understandable, authorising external audits.
5. Principle “under user control”: precluding a prescriptive approach and ensuring that users are informed actors and in control of their choices.

The Council of Europe ethical charter principles are outlined here, with a link to the ethical charter istelf.

Women Leading in AI (WLinAI) 10 recommendations (February 2019)

1. Introduce a regulatory approach governing the deployment of AI which mirrors that used for the pharmaceutical sector.
2. Establish an AI regulatory function working alongside the Information Commissioner’s Office and Centre for Data Ethics – to audit algorithms, investigate complaints by individuals,issue notices and fines for breaches of GDPR and equality and human rights law, give wider guidance, spread best practice and ensure algorithms must be fully explained to users and open to public scrutiny.
3. Introduce a new Certificate of Fairness for AI systems alongside a ‘kite mark’ type scheme to display it. Criteria to be defined at industry level, similarly to food labelling regulations.
4. Introduce mandatory AIAs (Algorithm Impact Assessments) for organisations employing AI systems that have a significant effect on individuals.
5. Introduce a mandatory requirement for public sector organisations using AI for particular purposes to inform citizens that decisions are made by machines, explain how the decision is reached and what would need to change for individuals to get a different outcome.
6. Introduce a ‘reduced liability’ incentive for companies that have obtained a Certificate of Fairness to foster innovation and competitiveness.
7. To compel companies and other organisations to bring their workforce with them – by publishing the impact of AI on their workforce and offering retraining programmes for employees whose jobs are being automated.
8. Where no redeployment is possible, to compel companies to make a contribution towards a digital skills fund for those employees
9. To carry out a skills audit to identify the wide range of skills required to embrace the AI revolution.
10. To establish an education and training programme to meet the needs identified by the skills audit, including content on data ethics and social responsibility. As part of that, we recommend the set up of a solid, courageous and rigorous programme to encourage young women and other underrepresented groups into technology.

Presented by the Women Leading in AI group at a meeting in parliament in February 2019, this report in Forbes by Noel Sharkey outlines both the group, their recommendations, and the meeting.

The NHS’s 10 Principles for AI + Data (February 2019)

1. Understand users, their needs and the context
2. Define the outcome and how the technology will contribute to it
3. Use data that is in line with appropriate guidelines for the purpose for which it is being used
4. Be fair, transparent and accountable about what data is being used
5. Make use of open standards
6. Be transparent about the limitations of the data used and algorithms deployed
7. Show what type of algorithm is being developed or deployed, the ethical examination of how the data is used, how its performance will be validated and how it will be integrated into health and care provision
8. Generate evidence of effectiveness for the intended use and value for money
9. Make security integral to the design
10. Define the commercial strategy

These principles are set out with full commentary and elaboration on Artificial Lawyer here.

IEEE General Principles of Ethical Autonomous and Intelligent Systems (A/IS) (March 2019)

1. Human Rights: A/IS shall be created and operated to respect, promote, and protect internationally recognized human rights.
2. Well-being: A/IS creators shall adopt increased human well-being as a primary success criterion for development.
3. Data Agency: A/IS creators shall empower individuals with the ability to access and securely share their data to maintain people’s capacity to have control over their identity.
4. Effectiveness: A/IS creators and operators shall provide evidence of the effectiveness and fitness for purpose of A/IS.
5. Transparency: the basis of a particular A/IS decision should always be discoverable.
6. Accountability: A/IS shall be created and operated to provide an unambiguous rationale for all decisions made.
7. Awareness of Misuse: A/IS creators shall guard against all potential misuses and risks of A/IS in operation.
8. Competence: A/IS creators shall specify and operators shall adhere to the knowledge and skill required for safe and effective operation.

These amended and extended general principles form part of Ethical Aligned Design 1st edition, published in March 2019. For an overview see pdf here.

Ethical issues arising from the police use of live facial recognition technology (March 2019)

9 ethical principles relate to: public interest, effectiveness, the avoidance of bias and algorithmic justice, impartiality and deployment, necessity, proportionality, impartiality, accountability, oversight, and the construction of watchlists, public trust, and cost effectiveness.

Reported here the UK government’s independent Biometrics and Forensics Ethics Group (BFEG) published an interim report outlining nine ethical principles forming a framework to guide policy on police facial recognition systems.

Floridi and Clement Jones’ five principles key to any ethical framework for AI (March 2019)

1. AI must be beneficial to humanity.
2. AI must also not infringe on privacy or undermine security.
3. AI must protect and enhance our autonomy and ability to take decisions and choose between alternatives.
4. AI must promote prosperity and solidarity, in a fight against inequality, discrimination, and unfairness
5. We cannot achieve all this unless we have AI systems that are understandable in terms of how they work (transparency) and explainable in terms of how and why they reach the conclusions they do (accountability).

Luciano Floridi and Lord Tim Clement Jones set out, here in the New Statesman, these 5 general ethical principles for AI, with additional commentary.

The European Commission’s High Level Expert Group on AI Ethics Guidelines for Trustworthy AI (April 2019)

1. Human agency and oversight AI systems should support human autonomy and decision-making, as prescribed by the principle of respect for human autonomy.
2. Technical robustness and safety A crucial component of achieving Trustworthy AI is technical robustness, which is closely linked to the principle of prevention of harm.
3. Privacy and Data governance Closely linked to the principle of prevention of harm is privacy, a fundamental right particularly affected by AI systems.
4. Transparency This requirement is closely linked with the principle of explicability and encompasses transparency of elements relevant to an AI system: the data, the system and the business models.
5. Diversity, non-discrimination and fairness In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system’s life cycle.
6. Societal and environmental well-being In line with the principles of fairness and prevention of harm, the broader society, other sentient beings and the environment should be also considered as stakeholders throughout the AI system’s life cycle.
7. Accountability The requirement of accountability complements the above requirements, and is closely linked to the principle of fairness

For more detail on each of these principles follow the links above.

Published on 8 April 2019, the EU HLEG AI ethics guidelines for trustworthy AI are detailed in full here.

Draft core principles of Australia’s Ethics Framework for AI (April 2019)

1. Generates net-benefits. The AI system must generate benefits for people that are greater than the costs.
2. Do no harm. Civilian AI systems must not be designed to harm or deceive people and should be implemented in ways that minimise any negative outcomes.
3. Regulatory and legal compliance. The AI system must comply with all relevant international, Australian Local, State/Territory and Federal government obligations, regulations and laws.
4. Privacy protection. Any system, including AI systems, must ensure people’s private data is protected and kept confidential plus prevent data breaches which could cause reputational, psychological, financial, professional or other types of harm.
5. Fairness. The development or use of the AI system must not result in unfair discrimination against individuals, communities or groups. This requires particular attention to ensure the “training data” is free from bias or characteristics which may cause the algorithm to behave unfairly.
6. Transparency & Explainability. People must be informed when an algorithm is being used that impacts them and they should be provided with information about what information the algorithm uses to make decisions.
7. Contestability. When an algorithm impacts a person there must be an efficient process to allow that person to challenge the use or output of the algorithm.
8. Accountability. People and organisations responsible for the creation and implementation of AI algorithms should be identifiable and accountable for the impacts of that algorithm, even if the impacts are unintended.

These draft principles are detailed in Artificial Intelligence Australia’s Ethics Framework A Discussion Paper. This comprehensive paper includes detailed summaries of many of the frameworks and initiatives listed above, together with some very useful case studies.

References

[1] Asimov, Isaac (1950): Runaround, in I, Robot, (The Isaac Asimov Collection ed.) Doubleday. ISBN 0-385-42304-7.
[2] Murphy, Robin; Woods, David D. (2009): Beyond Asimov: The Three Laws of Responsible Robotics. IEEE Intelligent systems. 24 (4): 14–20.
[3] Margaret Boden et al (2017): Principles of robotics: regulating robots in the real world
Connection Science. 29 (2): 124:129.
[4] Tony Prescott and Michael Szollosy (eds.) (2017): Ethical Principles of Robotics, Connection Science. 29 (2) and 29 (3).

The Pedestrian Experiment

Followers of this blog will know that I have been working for some years on simulation-based internal models - demonstrating their potential for ethical robots, safer robots and imitating robots. But pretty much all of our experiments so far have involved only one robot with a simulation-based internal model while the other robots it interacts with have no internal model at all.

But some time ago we wondered what would happen if two robots, each with a simulation-based internal model, interacted with each other. Imagine two such robots approaching each other in the same way that two pedestrians approach each other on the sidewalk. Is it possible that these 'pedestrian' robots might, from time to time, engage in the kind of 'dance' that human pedestrians do when one steps to their left and the other to their right only to compound the problem of avoiding a collision with a stranger? The answer, it turns out, is yes!

The idea was taken up by Mathias Schmerling at the Humboldt University of Berlin, adapting the code developed by Christian Blum for the Corridor experiment. Chen Yang, one of my masters students, has now updated Mathias' code and has produced some very nice new results.

Most of the time the pedestrian robots pass each other without fuss but in something between 1 in 5 and 1 in 10 trials we do indeed see an interesting dance. Here are a couple of examples of the majority of trials, when the robots pass each other normally, showing the robots' trajectories. In each trial blue starts from the left and green from the right. Note that there is an element of randomness in the initial directions of each robot (which almost certainly explains the relative occurrence of normal and dance behaviours).

And here is a gif animation showing what's going on in a normal trial. The faint straight lines from each robot show the target directions for each next possible action modelled in each robot's simulation-based internal model (consequence engine); the various dotted lines show the predicted paths (and possible collisions) and the solid blue and green lines show which next action is actually selected following the internal modelling.

Here is a beautiful example of a 'dance', again showing the robot trajectories. Note that the impasse resolves itself after awhile. We're still trying to figure out exactly what mechanism enables this resolution.

And here is the gif animation of the same trial:

Notice that the impasse is not resolved until the fifth turns of each robot.

Is this the first time that pedestrians passing each other - and in particular the occasional dance that ensues - has been computationally modelled?

All of the results above were obtained in simulation (yes there really are simulations within a simulation going on here), but within the past week Chen Yang has got this experiment working with real e-puck robots. Videos will follow shortly.

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Acknowledgements.

I am indebted to the brilliant experimental work of first Christian Blum (supported by Wenguo Liu), then Mathias Schmerling who adapted Christian's code for this experiment, and now Chen Yang who has developed the code further and obtained these results.

Simulation-based internal models for safer robots

Readers of this blog will know that I've become very excited by the potential of robots with simulation-based internal models in recent years. So far we've demonstrated their potential in simple ethical robots and as the basis for rational imitation. Our most recent publication instead examines the potential of robots with simulation-based internal models for safety. Of course it's not hard to see why the ability to model and predict the consequences of both your own and others' actions can help you to navigate the world more safely than without that ability.

Our paper Simulation-Based Internal Models for Safer Robots demonstrates the value of anticipation in what we call the corridor experiment. Here a smart robot (equipped with a simulation based internal model which we call a consequence engine) must navigate to the end of a corridor while maintaining a safe space around it at all times despite five other robots moving randomly in the corridor - in much the same way you and I might have to navigate down a busy office corridor while others are coming in the opposite direction.

Here is the abstract from our paper:

In this paper, we explore the potential of mobile robots with simulation-based internal models for safety in highly dynamic environments. We propose a robot with a simulation of itself, other dynamic actors and its environment, inside itself. Operating in real time, this simulation-based internal model is able to look ahead and predict the consequences of both the robot’s own actions and those of the other dynamic actors in its vicinity. Hence, the robot continuously modifies its own actions in order to actively maintain its own safety while also achieving its goal. Inspired by the problem of how mobile robots could move quickly and safely through crowds of moving humans, we present experimental results which compare the performance of our internal simulation-based controller with a purely reactive approach as a proof-of-concept study for the practical use of simulation-based internal models.

So, does it work? Thanks to some brilliant experimental work by Christian Blum the answer is a resounding yes. The best way to understand what's going on is with this wonderful gif animation of one experimental run below. The smart robot (blue) starts at the left and has the goal of safely reaching the right hand end of the corridor – its actual path is also shown in blue. Meanwhile 5 (red) robots are moving randomly (including bouncing off walls) and their actual paths are also shown in red; these robots are equipped only with simple obstacle avoidance behaviours. The larger blue circle shows blue's 'attention radius' – to reduce computational effort blue will only model red robots within this radius. The yellow paths in front of the red robots in blue's attention radius show blue's predictions of how those robots will move (taking into account collisions with the corridor walls and with blue and each other). The light blue projection in front of blue shows which of the 34 next possible actions of blue that is internally modelled is actually chosen as the next action (which, as you will see, sometimes includes standing still).

What do the results show us? Christian ran lots of trials – 88 simulations and 54 real robot experiments – over four experiments: (1) the baseline in simulation – in which the blue robot has only a simple reactive collision avoidance behaviour, (2) the baseline with real robots, (3) using the consequence engine (CE) in the blue robot in simulation, and (4) using the consequence engine in the blue robot with real robots. In the results below (a) shows the time taken for the blue robot to reach the end of the corridor, (b) shows the distance that the blue robot covers while reaching the end of the corridor, (c) shows the “danger ratio” experienced by the blue robot, and (d) shows the number of consequence engine runs per timestep in the blue robot. The danger ratio is the percentage of the run time that anther robot is within the blue robot’s safety radius.

For a relatively small cost in additional run time and distance covered, panels (a) and (b), the danger ratio is very significantly reduced from a mean value of ~20% to a mean value of zero, panel (c). Of course there is a computational cost, and this is reflected in panel (d); the baseline experiment has no consequence engine and hence runs no simulations, whereas the smart robot runs an average of between 8 and 10 simulations per time-step. This is exactly what we would expect: predicting the future clearly incurs a computational overhead.

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Full paper reference:

Blum C, Winfield AFT and Hafner VV (2018) Simulation-Based Internal Models for Safer Robots. Front. Robot. AI 4:74. doi: 10.3389/frobt.2017.00074

Acknowledgements:
I am indebted to Christian Blum who programmed the robots, set up the experiment and obtained the results outlined here. Christian lead authored the paper, which was also co-authored by my friend and research collaborator Verena Hafner, who was Christian's PhD advisor.

The imitation of goals

The imitation of goals is an extremely important form of social learning in humans. This is reflected in the early emergence of imitation in human infants; from the age of two, we humans can imitate both actions and their intended goals and this has been termed rational imitation.

Imitation has long been regarded as a compelling method for (social) learning in robots. However, robot imitation faces a number of challenges; one of the most fundamental is determining what to imitate. Although not trivial it is relatively straightforward to imitate actions — something we explored within the Artificial Culture project. But inferring goals from observed actions and thus determining which parts of a demonstrated sequence of actions are relevant, i.e., rational imitation, is very challenging.

The approach we take in our paper Rational imitation for robots: the cost difference model is to equip the imitating robot with a simulation-based internal model that allows the robot to explore alternative sequences of actions needed to attain the demonstrator robot’s potential goals (i.e., goals that are possible explanations for its observed actions). Comparing these actions with those observed in the demonstrator robot enables the imitating robot to infer the goals underlying those observed actions.

Here is the abstract from our paper:

Infants imitate behaviour flexibly. Depending on the circumstances, they copy both actions and their effects or only reproduce the demonstrator’s intended goals. In view of this selective imitation, infants have been called rational imitators. The ability to selectively and adaptively imitate behaviour would be a beneficial capacity for robots. Indeed, selecting what to imitate is one of the outstanding unsolved problems in the field of robotic imitation. In this paper, we first present a formalized model of rational imitation suited for robotic applications. Next, we test and demonstrate it using two humanoid robots.

My colleague Dieter Vanderelst conducted several experiments to demonstrate rational imitation. Let me outline one of them, which uses two NAO robots.

Here panels (a,d,g) show the setup with blue as the demonstrating robot and red the observing (then imitating) robot. Panels (b,e,h) Show trajectories of 3 runs of the demonstrator robot blue, and panels (c,f,i) show trajectories of 3 runs of the imitating robot red. Note that red always starts from the position it observes from, as you would if you were imitating your dance teacher.

In condition 1 blue moves directly to its goal position (panels a,b). Blue infers the goal is to move to red’s goal and does so directly in panel c.

In condition 2 blue deviates around an obstacle even though it has a direct path to its goal (panels d,e). In this case red infers that the deviation must be a sub-goal of blue — since blue is able to go directly to its goal but chooses not to — so in panel f red creates a trajectory via blue’s sub-goal. In other words red has correctly inferred blue’s intentions to imitate its goals.

In condition 3 blue’s path to its goal is blocked so it has no choice but to divert (panels g,h). In this case red infers that blue has no sub-goals and moves directly to the goal position (panel i).

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Full paper reference:
Vanderelst, D. and Winfield, A. F. (2017) Rational imitation for robots: The cost difference model. Adaptive Behavior, 25 (2). pp. 60-71. Download pdf.

Acknowledgements:
The experiments here were conceived and conducted by Dr Dieter Vanderelst, within EPSRC project Verifiable Autonomy.

Why ethical robots might not be such a good idea after all

This week my colleague Dieter Vanderelst presented our paper: The Dark Side of Ethical Robots at AIES 2018 in New Orleans.

I blogged about Dieter's very elegant experiment here, but let me summarise. With two NAO robots he set up a demonstration of an ethical robot helping another robot acting as a proxy human, then showed that with a very simple alteration of the ethical robot's logic it is transformed into a distinctly unethical robot - behaving either competitively or aggressively toward the proxy human.

Here are our paper's key conclusions:

The ease of transformation from ethical to unethical robot is hardly surprising. It is a straightforward consequence of the fact that both ethical and unethical behaviours require the same cognitive machinery with – in our implementation – only a subtle difference in the way a single value is calculated. In fact, the difference between an ethical (i.e. seeking the most desirable outcomes for the human) robot and an aggressive (i.e. seeking the least desirable outcomes for the human) robot is a simple negation of this value.

On the face of it, given that we can (at least in principle) build explicitly ethical machines* then it would seem that we have a moral imperative to do so; it would appear to be unethical not to build ethical machines when we have that option. But the findings of our paper call this assumption into serious doubt. Let us examine the risks associated with ethical robots and if, and how, they might be mitigated. There are three.

1. First there is the risk that an unscrupulous manufacturer might insert some unethical behaviours into their robots in order to exploit naive or vulnerable users for financial gain, or perhaps to gain some market advantage (here the VW diesel emissions scandal of 2015 comes to mind). There are no technical steps that would mitigate this risk, but the reputational damage from being found out is undoubtedly a significant disincentive. Compliance with ethical standards such as BS 8611 guide to the ethical design and application of robots and robotic systems, or emerging new IEEE P700X ‘human’ standards would also support manufacturers in the ethical application of ethical robots. 
2. Perhaps more serious is the risk arising from robots that have user adjustable ethics settings. Here the danger arises from the possibility that either the user or a technical support engineer mistakenly, or deliberately, chooses settings that move the robot’s behaviours outside an ‘ethical envelope’. Much depends of course on how the robot’s ethics are coded, but one can imagine the robot’s ethical rules expressed in a user-accessible format, for example, an XML like script. No doubt the best way to guard against this risk is for robots to have no user adjustable ethics settings, so that the robot’s ethics are hard-coded and not accessible to either users or support engineers. 
3. But even hard-coded ethics would not guard against undoubtedly the most serious risk of all, which arises when those ethical rules are vulnerable to malicious hacking. Given that cases of white-hat hacking of cars have already been reported, it's not difficult to envisage a nightmare scenario in which the ethics settings for an entire fleet of driverless cars are hacked, transforming those vehicles into lethal weapons. Of course, driverless cars (or robots in general) without explicit ethics are also vulnerable to hacking, but weaponising such robots is far more challenging for the attacker. Explicitly ethical robots focus the robot’s behaviours to a small number of rules which make them, we think, uniquely vulnerable to cyber-attack.

Ok, taking the most serious of these risks: hacking, we can envisage several technical approaches to mitigating the risk of malicious hacking of a robot’s ethical rules. One would be to place those ethical rules behind strong encryption. Another would require a robot to authenticate its ethical rules by first connecting to a secure server. An authentication failure would disable those ethics, so that the robot defaults to operating without explicit ethical behaviours. Although feasible, these approaches would be unlikely to deter the most determined hackers, especially those who are prepared to resort to stealing encryption or authentication keys.

It is very clear that guaranteeing the security of ethical robots is beyond the scope of engineering and will need regulatory and legislative efforts. Considering the ethical, legal and societal implications of robots, it becomes obvious that robots themselves are not where responsibility lies. Robots are simply smart machines of various kinds and the responsibility to ensure they behave well must always lie with human beings. In other words, we require ethical governance, and this is equally true for robots with or without explicit ethical behaviours.

Two years ago I thought the benefits of ethical robots outweighed the risks. Now I'm not so sure. I now believe that - even with strong ethical governance - the risks that a robot’s ethics might be compromised by unscrupulous actors are so great as to raise very serious doubts over the wisdom of embedding ethical decision making in real-world safety critical robots, such as driverless cars. Ethical robots might not be such a good idea after all.

*As a footnote let me explain what I mean by explicitly ethical robots: these are robots that select behaviours on the basis of ethical rules - in a sense they can be said to reason about ethics (in our case by evaluating the ethical consequences of several possible actions). Here I'm using the terminology of James Moor, who proposed four kinds of ethical agents, as I explain here. Moor shows in his classification that all robots (and AIs) are ethical agents in the sense that they can all have an ethical impact.

Thus, even though we're calling into question the wisdom of explicitly ethical robots, that doesn't change the fact that we absolutely must design all robots to minimise the likelihood of ethical harms, in other words we should be designing implicitly ethical robots within Moor's schema.

Here is the full reference to our paper:

Vanderelst D and Winfield AFT (2018), The Dark Side of Ethical Robots, AAAI/ACM Conf. on AI Ethics and Society (AIES 2018), New Orleans.

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Related blog posts:
The Dark side of Ethical Robots
Could we make a moral machine?
How ethical is your ethical robot?
Towards ethical robots: an update
Towards an Ethical Robot