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Roger Clarke's 'Principles for AI'

Principles for AI: A SourceBook

Revision of 18 October 2018
(Insertion of 2 and 3, Expansion of 9, Re-formatting; Insertion of 1; Insertion of 17)

Prepared in support of Guidelines for the Responsible Business Use of AI

Roger Clarke **

© Xamax Consultancy Pty Ltd, 2018

Available under an AEShareNet Free
for Education licence or a Creative Commons 'Some
Rights Reserved' licence.

This document is at http://www.rogerclarke.com/EC/GAIP.html


Introduction

IT suppliers, and business and government user organisations, are terrified by the prospect of regulation constraining their activities. Recent claims by purveyors of 'Artificial Intelligence' (AI) notions have been met with widespread revulsion from the public. In an endeavour to calm the public's nerves, a wide variety of organisations have rapidly published 'principles' and 'guidelines' which those organisations claim will mitigate the harm that AI will cause (or would cause, if AI actually delivers on its promises this time around).

On the one hand, collections of 'principles' and 'guidance' will do little or nothing to exercise control over AI research, development and deployment, because they are merely window-dressing. For example:

On the other hand, many of these documents have been developed by well-resourced organisations that have access to researchers, developers and implementors of various AI technologies. Great care must be taken to appreciate sub-texts, to consider why statements are framed as they are, to understand the effects of qualifying words, and to identify aspects that are entirely missing. Provided that appropriate scepticism is brought to the activity, however, there is value to be extracted from these documents.


Contents

  1. Asimov (1942, 1993)
  2. British Standards Institute (2016)
  3. European Parliament (2016)
  4. The Greens / European Free Alliance Digital Working Group (Nov 2016)
  5. IBM (Jan 2017)
  6. Future of Life Institute (Jan 2017)
  7. Association for Computing Machinery - ACM (Jan 2017)
  8. Internet Society (Apr 2017)
  9. Japanese Ministry of Internal Affairs and Communications (Oct 2017)
  10. Information Technology Industry Council (Oct 2017)
  11. UNI Global Union (Dec 2017)
  12. IEEE (Dec 2017)
  13. House of Lords (Apr 2018)
  14. Partnership on AI (Apr 2018)
  15. Google (Jun 2018)
  16. Microsoft (Aug 2018)
  17. The Public Voice (Oct 2018)

References


1. Asimov (1942, 1993)
Asimov's Laws of Robotics (Asimov 1942), as extended by Asimov's fiction (1942-1992), as interpreted in Clarke (1993)
(classified as a non-governmental organisation)

  1. The Meta-Law
    A robot may not act unless its actions are subject to the Laws of Robotics
  2. Law Zero
    A robot may not injure humanity, or, through inaction, allow humanity to come to harm
  3. Law One
    A robot may not injure a human being, or, through inaction, allow a human being to come to harm, unless this would violate a higher-order Law
  4. Law Two
    (a) A robot must obey orders given it by human beings, except where such orders would conflict with a higher-order Law
    (b) A robot must obey orders given it by superordinate robots, except where such orders would conflict with a higher-order Law
  5. Law Three
    (a) A robot must protect the existence of a superordinate robot as long as such protection does not conflict with a higher-order Law
    (b) A robot must protect its own existence as long as such protection does not conflict with a higher-order Law
  6. Law Four
    A robot must perform the duties for which it has been programmed, except where that would conflict with a higher-order law
  7. The Procreation Law
    A robot may not take any part in the design or manufacture of a robot unless the new robot's actions are subject to the Laws of Robotics

2. British Standards Institute (2016)
Guide to the ethical design and application of robots and robotic systems (BS 2016), as reported in Devlin (2016)
(classified as an industry association)

  1. Robots should not be designed solely or primarily to kill or harm humans
  2. Humans, not robots, are the responsible agents
  3. It should be possible to find out who is responsible for any robot and its behaviour
  4. Designers should aim for transparency
  5. Care is needed in relation to deceptive conduct by robots

3. European Parliament (2016)
Recommendations on Civil Law Rules on Robotics CLA-EP (2016)
(classified as a governmental organisation)

  1. Beneficence
    Robots should act in the best interests of humans
  2. Non-maleficence
    The doctrine of 'first, do no harm', whereby robots should not harm a human
  3. Autonomy
    The capacity to make an informed, un-coerced decision about the terms of interaction with robots
  4. Justice
    Fair distribution of the benefits associated with robotics and affordability of homecare and healthcare robots in particular.
  5. Fundamental Rights
    Robotics research activities should respect fundamental rights and be conducted in the interests of the well-being of individuals and society in their design, implementation, dissemination and use. Human dignity - both physical and psychological - is always to be respected.
  6. Precaution
    Robotics research activities should be conducted in accordance with the precautionary principle, anticipating potential safety impacts of outcomes and taking due precautions, proportional to the level of protection, while encouraging progress for the benefit of society and the environment.
  7. Inclusiveness
    Robotics engineers guarantee transparency and respect for the legitimate right of access to information by all stakeholders. Inclusiveness allows for participation in decision-making processes by all stakeholders involved in or concerned by robotics research activities.
  8. Accountability
    Robotics engineers should remain accountable for the social, environmental and human health impacts that robotics may impose on present and future generations.
  9. Safety
    Robot designers should consider and respect people's physical wellbeing, safety, health and rights. A robotics engineer must preserve human wellbeing, while also respecting human rights, and disclose promptly factors that might endanger the public or the environment.
  10. Reversibility
    Reversibility, being a necessary condition of controllability, is a fundamental concept when programming robots to behave safely and reliably. A reversibility model tells the robot which actions are reversible and how to reverse them if they are. The ability to undo the last action or a sequence of actions allows users to undo undesired actions and get back to the 'good' stage of their work.
  11. Privacy
    The right to privacy must always be respected. A robotics engineer should ensure that private information is kept secure and only used appropriately. Moreover, a robotics engineer should guarantee that individuals are not personally identifiable, aside from exceptional circumstances and then only with clear, unambiguous informed consent. Human informed consent should be pursued and obtained prior to any man-machine interaction. As such, robotics designers have a responsibility to develop and follow procedures for valid consent, confidentiality, anonymity, fair treatment and due process. Designers will comply with any requests that any related data be destroyed, and removed from any datasets.
  12. Maximising benefit and minimising harm
    Researchers should seek to maximise the benefits of their work at all stages, from inception through to dissemination. Harm to research participants/human subject/an experiment, trial, or study participant or subject must be avoided. Where risks arise as an unavoidable and integral element of the research, robust risk assessment and management protocols should be developed and complied with. Normally, the risk of harm should be no greater than that encountered in ordinary life, i.e. people should not be exposed to risks greater than or additional to those to which they are exposed in their normal lifestyles. The operation of a robotics system should always be based on a thorough risk assessment process, which should be informed by the precautionary and proportionality principles.

4. The Greens / European Free Alliance Digital Working Group (Nov 2016)
Position on Robotics and AI (GEFA 2016)
(classified as a non-governmental organisation)

  1. An informed public debate
    Public input and an informed debate is of the utmost importance, with the aim of shaping the technological revolution so that it serves humanity with a series of rules, governing, in particular, liability and ethics
  2. Precautionary principle
    Robots and artificial intelligence should be developed and produced based on an impact assessment, to the best available technical standards regarding security and with the possibility to intervene. Apply the precautionary principle and assess the long term ethical implications of new technologies in the early phase of their development
  3. Do no harm-principle
    Robots should not be designed to kill or harm humans. Their use must take place according to guaranteed individual rights and fundamental rights, including privacy by design and in particular human integrity, human dignity and identity. We underline the primacy of the human being over the sole interest of science or society.
  4. Ecological footprint
    Apply the principles of regenerative design, increase energy efficiency by promoting the use of renewable technologies for robotics, the use and reuse of secondary raw materials, and the reduction of waste
  5. Enhancements
    The provision of social or health services should not depend on the acceptance of robotics and artificial intelligence as implants or extensions to the human body. Inclusion and diversity must be the highest priority of our societies. The dignity of persons with or without disabilities is inviolable.
  6. Autonomy of persons
    The right to information and consent must be protected, including the protection of persons who are not able to consent. We reject the notion of "data ownership", which would run counter to data protection as a fundamental right and treat data as a tradable commodity
  7. Clear liabilities
    Legal responsibility should be attributed to a person. Regarding safety and security, producers shall be held responsible despite any existing non-liability clauses in user agreements. The unintended nature of possible damages should not automatically exonerate manufacturers, programmers or operators from their liability and responsibility. In order to reduce possible repercussions of failure and malfunctioning of sufficiently complex systems, we think that strict liability concepts should be evaluated, including compulsory insurance policies.
  8. Open environment
    We promote an open environment, from open standards and innovative licensing models, to open platforms and transparency, in order to avoid vendor lock-in that restrains interoperability
  9. Product safety
    Design robotics artificial intelligence products to be safe, secure and fit for purpose. Robots and AI should not exploit vulnerable users.
  10. Funding
    The European Union and its Member States should fund research to that end in particular with regards to the ethical and legal effects of artificial intelligence.

5. IBM (Jan 2017)
Guiding Principles for Ethical AI, as reported in Rayome (2017)
(classified as a corporation)

1. Purpose

Rometty said it's important for people to develop trust in an AI system. For IBM, the purpose of AI will be to aid humans, not replace them. "We say cognitive, not AI, because we are augmenting intelligence," Rometty said. "For most of our businesses and companies, it will not be man or machine... it will be a symbiotic relationship. Our purpose is to augment and really be in service of what humans do."

2. Transparency

You must be clear as you build AI platforms how they are trained, and what data was used in training. "The human needs to remain in control of the system," Rometty said. These systems will not have self-awareness or consciousness, she added.

And industry domain matters, Rometty added. With Watson, institutions can combine their decades of knowledge with industry data. "These systems will be most effective when trained with domain knowledge in an industry context," Rometty said.

3. Skills

AI platforms must be built with people in the industry, be they doctors, teachers, or underwriters. And companies must prepare to train human workers on how to use these tools to their advantage.

For example, Watson's oncology advisor is now rolling out in India, China, Thailand, Finland, and the Netherlands. It was trained by the world's best oncologists, IBM claims. "You get this reach when those principles are followed, and that to me is the great promise," Rometty said. "The reason this is worth fighting so strongly to roll out right is you can really solve problems. India has one oncologist for 1,600 patients."


6. Future of Life Institute (Jan 2017)
Asilomar AI Principles (FLI 2017)
(classified as a joint association)

Research Issues

1) Research Goal: The goal of AI research should be to create not undirected intelligence, but beneficial intelligence.

2) Research Funding: Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:

3) Science-Policy Link: There should be constructive and healthy exchange between AI researchers and policy-makers.

4) Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI.

5) Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards.

Ethics and Values

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.

16) Human Control: Humans should choose how and whether to delegate decisions to AI systems, to accomplish human-chosen objectives.

17) Non-subversion: The power conferred by control of highly advanced AI systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.

18) AI Arms Race: An arms race in lethal autonomous weapons should be avoided.

Longer-term Issues

19) Capability Caution: There being no consensus, we should avoid strong assumptions regarding upper limits on future AI capabilities.

20) Importance: Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources.

21) Risks: Risks posed by AI systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.

22) Recursive Self-Improvement: AI systems designed to recursively self-improve or self-replicate in a manner that could lead to rapidly increasing quality or quantity must be subject to strict safety and control measures.

23) Common Good: Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organization.


7. Association for Computing Machinery (Jan 2017)
Principles for Algorithmic Transparency and Accountability (ACM 2017)
(classified as a professional association)

  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. Public scrutiny of the data provides maximum opportunity for corrections. However, concerns over privacy, protecting trade secrets, or revelation of analytics that might allow malicious actors to game the system can justify restricting access to qualified and authorized individuals.
  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. In particular, they should routinely perform tests to assess and determine whether the model generates discriminatory harm. Institutions are encouraged to make the results of such tests public.

8. Internet Society (Apr 2017)
Guiding Principles and Recommendations re AI and Machine Learning (ISOC 2017)
(classified as a non-governmental organisation)

  1. Ethical Considerations in Deployment and Design
    AI system designers and builders need to apply a user-centric approach to the technology. They need to consider their collective responsibility in building AI systems that will not pose security risks to the Internet and Internet users - Adopt ethical standards
  2. Ensure Interpretability of AI systems
    Decisions made by an AI agent should be possible to understand, especially if those decisions have implications for public safety, or result in discriminatory practices - Ensure Human Interpretability of Algorithmic Decisions and Empower Users
  3. Public Empowerment
    The public's ability to understand AI-enabled services, and how they work, is key to ensuring trust in the technology - 'Algorithmic Literacy' must be a basic skill, and Provide the public with information
  4. Responsible Deployment
    The capacity of an AI agent to act autonomously, and to adapt its behavior over time without human direction, calls for significant safety checks before deployment, and ongoing monitoring - Humans must be in control, Make safety a priority, Privacy is key, Think before you act, If they are connected, they must be secured, Responsible disclosure
  5. Ensuring Accountability
    Legal accountability has to be ensured when human agency is replaced by decisions of AI agents - Ensure legal certainty, Put users first and Assign liability up-front
  6. Social and Economic Impacts
    Stakeholders should shape an environment where AI provides socio-economic opportunities for all - All stakeholders should engage in an ongoing dialogue
  7. Open Governance
    The ability of various stakeholders, whether civil society, government, private sector or academia and the technical community, to inform and participate in the governance of AI is crucial for its safe deployment - Promote Multistakeholder Governance

9. Japanese Ministry of Internal Affairs and Communications (Oct 2017)
AI R&D Guidelines, as reported in Hirano (2017)
(classified as a governmental organisation)

  1. I. Collaboration
    Pay attention to the interconnectivity and interoperability of AI systems.
  2. II. Transparency
    Pay attention to the verifiability of inputs/outputs of AI systems and explainability of their decisions.
  3. III. Controllability
    Pay attention to the controllability of AI systems.
  4. IV. Safety
    Take it into consideration that AI systems will not harm the life, body, or property of users or third parties through actuators or other devices.
  5. V. Security
    Pay attention to the security of AI systems.
  6. VI. Privacy
    Take it into consideration that AI systems will not infringe the privacy of users or third parties.
  7. VII. Ethics
    Respect human dignity and individual autonomy in R&D of AI systems.
  8. VIII. User Assistance
    Take it into consideration that AI systems will support users and make it possible to give them opportunities for choice in appropriate manners.
  9. IX. Accountability
    Make efforts to fulfill their accountability to stakeholders including users of AI systems.

10. Information Technology Industry Council (Oct 2017)
AI Policy Principles (ITIC 2017)
(classified as an industry association)

  1. Responsible Design and Deployment
    We recognize our responsibility to integrate principles into the design of AI technologies, beyond compliance with existing laws. While the potential benefits to people and society are amazing, AI researchers, subject matter experts, and stakeholders should and do spend a great deal of time working to ensure the responsible design and deployment of AI systems. Highly autonomous AI systems must be designed consistent with international conventions that preserve human dignity, rights, and freedoms. As an industry, it is our responsibility to recognize potentials for use and misuse, the implications of such actions, and the responsibility and opportunity to take steps to avoid the reasonably predictable misuse of this technology by committing to ethics by design.
  2. Safety and Controllability
    Technologists have a responsibility to ensure the safe design of AI systems. Autonomous AI agents must treat the safety of users and third parties as a paramount concern, and AI technologies should strive to reduce risks to humans. Furthermore, the development of autonomous AI systems must have safeguards to ensure controllability of the AI system by humans, tailored to the specific context in which a particular system operates.
  3. Robust and Representative Data
    To promote the responsible use of data and ensure its integrity at every stage, industry has a responsibility to understand the parameters and characteristics of the data, to demonstrate the recognition of potentially harmful bias, and to test for potential bias before and throughout the deployment of AI systems. AI systems need to leverage large datasets, and the availability of robust and representative data for building and improving AI and machine learning systems is of utmost importance.
  4. Interpretability
    We are committed to partnering with others across government, private industry, academia, and civil society to find ways to mitigate bias, inequity, and other potential harms in automated decision-making systems. Our approach to nding such solutions should be tailored to the unique risks presented by the specific context in which a particular system operates. In many contexts, we believe tools to enable greater interpretability will play an important role.
  5. Liability of AI Systems Due to Autonomy
    The use of AI to make autonomous consequential decisions about people, informed by - but often replacing decisions made by - human-driven bureaucratic processes, has led to concerns about liability. Acknowledging existing legal and regulatory frameworks, we are committed to partnering with relevant stakeholders to inform a reasonable accountability framework for all entities in the context of autonomous systems.

11. UNI Global Union (Dec 2017)
Top 10 Principles for Ethical AI (UGU 2017)
(classified as a non-governmental organisation)

  1. Demand That AI Systems Are Transparent
    A transparent artificial intelligence system is one in which it is possible to discover how, and why, the system made a decision, or in the case of a robot, acted the way it did.
  2. Equip AI Systems With an 'Ethical Black Box'
    Full transparency in an AI system should be facilitated by the presence of a device that can record information about said system in the form of an 'ethical black box' that not only contains relevant data to ensure transparency and accountability of a system, but also includes clear data and information on the ethical considerations built into said system.
  3. Make AI Serve People and Planet
    This includes codes of ethics for the development, application and use of AI so that throughout their entire operational process, AI systems remain compatible and increase the principles of human dignity, integrity, freedom, privacy and cultural and gender diversity, as well as with fundamental human rights. In addition, AI systems must protect and even improve our planet's ecosystems and biodiversity.
  4. Adopt a Human-In-Command Approach
    An absolute precondition is that the development of AI must be responsible, safe and useful, where machines maintain the legal status of tools, and legal persons retain control over, and responsibility for, these machines at all times.
  5. Ensure a Genderless, Unbiased AI
    In the design and maintenance of AI, it is vital that the system is controlled for negative or harmful human-bias, and that any bias - be it gender, race, sexual orientation, age, etc. - is identified and is not propagated by the system.
  6. Share the Benefits of AI Systems
    AI technologies should benefit and empower as many people as possible. The economic prosperity created by AI should be distributed broadly and equally, to benefit all of humanity.
  7. Secure a Just Transition and Ensuring Support for Fundamental Freedoms and Rights
    As AI systems develop and augmented realities are formed, workers and work tasks will be displaced. To ensure a just transition, as well as sustainable future developments, it is vital that corporate policies are put in place that ensure corporate accountability in relation to this displacement, such as retraining programmes and job change possibilities. Governmental measures to help displaced workers retrain and nd new employment are additionally required.
  8. Establish Global Governance Mechanisms
    UNI recommends the establishment of multi-stakeholder Decent Work and Ethical AI governance bodies on global and regional levels. The bodies should include AI designers, manufacturers, owners, developers, researchers, employers, lawyers, CSOs and trade unions. Whistleblowing mechanisms and monitoring procedures to ensure the transition to, and implementation of, ethical AI must be established. The bodies should be granted the competence to recommend compliance processes and procedures.
  9. Ban the Attribution of Responsibility to Robots
    Robots should be designed and operated as far as is practicable to comply with existing laws, fundamental rights and freedoms, including privacy. This is linked to the question of legal responsibility. In line with Bryson et al 2011, UNI Global Union asserts that legal responsibility for a robot should be attributed to a person. Robots are not responsible parties under the law.
  10. Ban AI Arms Race
    Lethal autonomous weapons, including cyber warfare, should be banned.

12. IEEE (Dec 2017)
Principles for Autonomous and Intelligent Systems (A/IS) (IEEE 2017, pp.6-7, 22-32)
(classified as a professional association)

Principle 1 -- Human Rights

(1) Governance frameworks, including standards and regulatory bodies, should be established to oversee processes assuring that the use of A/IS does not infringe upon human rights, freedoms, dignity, and privacy, and of traceability to contribute to the building of public trust in A/IS.

(2) A way to translate existing and forthcoming legal obligations into informed policy and technical considerations is needed. Such a method should allow for differing cultural norms as well as legal and regulatory frameworks.

(3) For the foreseeable future, A/IS should not be granted rights and privileges equal to human rights, A/IS should always be subordinate to human judgment and control.

Principle 2 -- Prioritizing Well-being

A/IS should prioritize human well-being as an outcome in all system designs, using the best available, and widely accepted, well-being metrics as their reference point. [The discussion appears to be primarily concerned with economic wellbeing]

Principle 3 -- Accountability

(1) Legislatures/courts should clarify issues of responsibility, culpability, liability, and accountability for A/IS where possible during development and deployment (so that manufacturers and users understand their rights and obligations).

(2) Designers and developers of A/IS should remain aware of, and take into account when relevant, the diversity of existing cultural norms among the groups of users of these A/IS.

(3) Multi-stakeholder ecosystems should be developed to help create norms (which can mature to best practices and laws) where they do not exist ... (including representatives of civil society, law enforcement, insurers, manufacturers, engineers, lawyers, etc.).

(4) Systems for registration and record-keeping should be created so that it is always possible to find out who is legally responsible for a particular A/IS. Manufacturers/operators/ owners of A/IS should register key, high-level parameters, including Training data/training environment (if applicable), Sensors/real world data sources, Algorithms, Process graphs, Model features (at various levels), User interfaces, Actuators/outputs, Optimization goal/loss function/reward function

Principle 4 -- Transparency

Develop new standards that describe measurable, testable levels of transparency, so that systems can be objectively assessed and levels of compliance determined. For designers, such standards will provide a guide for self-assessing transparency during development and suggest mechanisms for improving transparency.

Principle 5 -- A/IS Technology Misuse and Awareness of it

Minimize the risks of misuse of A/IS by raising public awareness, providing ethics education, and educating government, lawmakers and enforcement agencies [but with no mention of obligations, sanctions and enforcement]


13. House of Lords Select Committee on Artificial Intelligence (Apr 2018)
Core Principles for AI, buried inside HoL (2018),
as extracted for the World Economic Forum (WEF) (Smith 2018)
(classified as a governmental organisation)

A WEF document claims that these "core principles" derive from a report commissioned by the House of Lords AI Select Committee, which is based on evidence from over 200 industry experts - most of whom presumably has at least a degree of self-interest in the outcome.

(1) AI must be a force for good - and diversity

The first principle argues that AI should be developed for the common good and benefit of humanity.

The report's authors argue the United Kingdom must actively shape the development and utilisation of AI, and call for "a shared ethical AI framework" that provides clarity against how this technology can best be used to benefit individuals and society.

They also say the prejudices of the past must not be unwittingly built into automated systems, and urge that such systems "be carefully designed from the beginning, with input from as diverse a group of people as possible".

(2) Intelligibility and fairness

The second principle demands that AI operates within parameters of intelligibility and fairness, and calls for companies and organisations to improve the intelligibility of their AI systems.

"Without this, regulators may need to step in and prohibit the use of opaque technology in significant and sensitive areas of life and society", the report warns.

(3) No Diminution of Data Rights or Privacy

Third, the report says artificial intelligence should not be used to diminish the data rights or privacy of individuals, families or communities.

It says the ways in which data is gathered and accessed need to be reconsidered. This, the report says, is designed to ensure companies have fair and reasonable access to data, while citizens and consumers can also protect their privacy.

"Large companies which have control over vast quantities of data must be prevented from becoming overly powerful within this landscape. We call on the government ... to review proactively the use and potential monopolisation of data by big technology companies operating in the UK".

(4) Flourishing alongside AI

The fourth principle stipulates all people should have the right to be educated as well as be enabled to flourish mentally, emotionally and economically alongside artificial intelligence.

For children, this means learning about using and working alongside AI from an early age. For adults, the report calls on government to invest in skills and training to negate the disruption caused by AI in the jobs market.

(5) Confronting the power to destroy

Fifth, and aligning with concerns around killer robots, the report says the autonomous power to hurt, destroy or deceive human beings should never be vested in artificial intelligence.

"There is a significant risk that well-intended AI research will be misused in ways which harm people," the report says. "AI researchers and developers must consider the ethical implications of their work".


14. Partnership on AI (Apr 2018)
Our Work (Thematic Pillars) (PoAI 2018)
(classified as a joint association)

1 Safety-Critical AI

Advances in AI have the potential to improve outcomes, enhance quality, and reduce costs in such safety-critical areas as healthcare and transportation. Effective and careful applications of pattern recognition, automated decision making, and robotic systems show promise for enhancing the quality of life and preventing thousands of needless deaths.

However, where AI tools are used to supplement or replace human decision-making, we must be sure that they are safe, trustworthy, and aligned with the ethics and preferences of people who are influenced by their actions.

We will pursue studies and best practices around the fielding of AI in safety-critical application areas.

2 Fair, Transparent, and Accountable AI

AI has the potential to provide societal value by recognizing patterns and drawing inferences from large amounts of data. Data can be harnessed to develop useful diagnostic systems and recommendation engines, and to support people in making breakthroughs in such areas as biomedicine, public health, safety, criminal justice, education, and sustainability.

While such results promise to provide real benefits, we need to be sensitive to the possibility that there are hidden assumptions and biases in data, and therefore in the systems built from that data - in addition to a wide range of other system choices which can be impacted by biases, assumptions, and limits. This can lead to actions and recommendations that replicate those biases, and have serious blind spots.

Researchers, officials, and the public should be sensitive to these possibilities and we should seek to develop methods that detect and correct those errors and biases, not replicate them. We also need to work to develop systems that can explain the rationale for inferences.

We will pursue opportunities to develop best practices around the development and fielding of fair, explainable, and accountable AI systems.

3 AI, Labor, and the Economy

AI advances will undoubtedly have multiple influences on the distribution of jobs and nature of work. While advances promise to inject great value into the economy, they can also be the source of disruptions as new kinds of work are created and other types of work become less needed due to automation.

Discussions are rising on the best approaches to minimizing potential disruptions, making sure that the fruits of AI advances are widely shared and competition and innovation are encouraged and not stifled. We seek to study and understand best paths forward, and play a role in this discussion.

4 Collaborations Between People and AI Systems

A promising area of AI is the design of systems that augment the perception, cognition, and problem-solving abilities of people. Examples include the use of AI technologies to help physicians make more timely and accurate diagnoses and assistance provided to drivers of cars to help them to avoid dangerous situations and crashes.

Opportunities for R&D and for the development of best practices on AI-human collaboration include methods that provide people with clarity about the understandings and confidence that AI systems have about situations, means for coordinating human and AI contributions to problem solving, and enabling AI systems to work with people to resolve uncertainties about human goals.

5 Social and Societal Influences of AI

AI advances will touch people and society in numerous ways, including potential influences on privacy, democracy, criminal justice, and human rights. For example, while technologies that personalize information and that assist people with recommendations can provide people with valuable assistance, they could also inadvertently or deliberately manipulate people and influence opinions.

We seek to promote thoughtful collaboration and open dialogue about the potential subtle and salient influences of AI on people and society.

6 AI and Social Good

AI offers great potential for promoting the public good, for example in the realms of education, housing, public health, and sustainability. We see great value in collaborating with public and private organizations, including academia, scientific societies, NGOs, social entrepreneurs, and interested private citizens to promote discussions and catalyze efforts to address society's most pressing challenges.

Some of these projects may address deep societal challenges and will be moonshots - ambitious big bets that could have far-reaching impacts. Others may be creative ideas that could quickly produce positive results by harnessing AI advances.


15. Google (Jun 2018)
Objectives for AI applications (Pichai 2018)
(classified as a corporation)

We will assess AI applications in view of the following objectives. We believe that AI should:

1. Be socially beneficial.

The expanded reach of new technologies increasingly touches society as a whole. Advances in AI will have transformative impacts in a wide range of fields, including healthcare, security, energy, transportation, manufacturing, and entertainment. As we consider potential development and uses of AI technologies, we will take into account a broad range of social and economic factors, and will proceed where we believe that the overall likely benefits substantially exceed the foreseeable risks and downsides.

AI also enhances our ability to understand the meaning of content at scale. We will strive to make high-quality and accurate information readily available using AI, while continuing to respect cultural, social, and legal norms in the countries where we operate. And we will continue to thoughtfully evaluate when to make our technologies available on a non-commercial basis.

2. Avoid creating or reinforcing unfair bias.

AI algorithms and datasets can reflect, reinforce, or reduce unfair biases. We recognize that distinguishing fair from unfair biases is not always simple, and differs across cultures and societies. We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief.

3. Be built and tested for safety.

We will continue to develop and apply strong safety and security practices to avoid unintended results that create risks of harm. We will design our AI systems to be appropriately cautious, and seek to develop them in accordance with best practices in AI safety research. In appropriate cases, we will test AI technologies in constrained environments and monitor their operation after deployment.

4. Be accountable to people.

We will design AI systems that provide appropriate opportunities for feedback, relevant explanations, and appeal. Our AI technologies will be subject to appropriate human direction and control.

5. Incorporate privacy design principles.

We will incorporate our privacy principles in the development and use of our AI technologies. We will give opportunity for notice and consent, encourage architectures with privacy safeguards, and provide appropriate transparency and control over the use of data.

6. Uphold high standards of scientific excellence.

Technological innovation is rooted in the scientific method and a commitment to open inquiry, intellectual rigor, integrity, and collaboration. AI tools have the potential to unlock new realms of scientific research and knowledge in critical domains like biology, chemistry, medicine, and environmental sciences. We aspire to high standards of scientific excellence as we work to progress AI development.

We will work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. And we will responsibly share AI knowledge by publishing educational materials, best practices, and research that enable more people to develop useful AI applications.

7. Be made available for uses that accord with these principles.

Many technologies have multiple uses. We will work to limit potentially harmful or abusive applications. As we develop and deploy AI technologies, we will evaluate likely uses in light of the following factors:

AI applications we will not pursue

In addition to the above objectives, we will not design or deploy AI in the following application areas:

We want to be clear that while we are not developing AI for use in weapons, we will continue our work with governments and the military in many other areas. These include cybersecurity, training, military recruitment, veterans, healthcare, and search and rescue. These collaborations are important and we'll actively look for more ways to augment the critical work of these organizations and keep service members and civilians safe.

o--o--o--o--o--o--o--o

Google's announcement was met with immediate scepticism (Newcomer 2018): ""[With the exception of not working on "technologies whose principal purpose or implementation is to cause or directly facilitate injury to people], the rest of the company's "principles" are peppered with lawyerly hedging and vague commitments ... Without promising independent oversight, Google is just putting a new, less persuasive, spin on an old principle it's tried to bury: 'Don't be evil'".


16. Microsoft's AI Principles (Aug 2018)
(MS 2018)
(classified as a corporation)

We're excited about the opportunities that AI brings to people and its ability to help us achieve more. But it's also important to us that we build upon an ethical foundation. We believe that AI technology should embody the following four principles:

  1. Fairness
    AI must maximize efficiencies without destroying dignity and guard against bias
  2. Accountability
    AI must have algorithmic accountability
  3. Transparency
    AI must be transparent
  4. Ethics
    AI must assist humanity and be designed for intelligent privacy

17. The Public Voice's Universal Guidelines for AI (Oct 2018)
(TPV 2018)
(classified as a non-governmental organisation)

New developments in Artificial Intelligence are transforming the world, from science and industry to government administration and finance. The rise of AI decision-making also implicates fundamental rights of fairness, accountability, and transparency. Modern data analysis produces significant outcomes that have real life consequences for people in employment, housing, credit, commerce, and criminal sentencing. Many of these techniques are entirely opaque, leaving individuals unaware whether the decisions were accurate, fair, or even about them.

We propose these Universal Guidelines to inform and improve the design and use of AI. The Guidelines are intended to maximize the benefits of AI, to minimize the risk, and to ensure the protection of human rights. These Guidelines should be incorporated into ethical standards, adopted in national law and international agreements, and built into the design of systems. We state clearly that the primary responsibility for AI systems must reside with those institutions that fund, develop, and deploy these systems.

  1. Right to Transparency. All individuals have the right to know the basis of an AI decision that concerns them. This includes access to the factors, the logic, and techniques that produced the outcome.
  2. Right to Human Determination. All individuals have the right to a final determination made by a person.
  3. Identification Obligation. The institution responsible for an AI system must be made known to the public.
  4. Fairness Obligation. Institutions must ensure that AI systems do not reflect unfair bias or make impermissible discriminatory decisions.
  5. Assessment and Accountability Obligation. An AI system should only be deployed after an adequate evaluation of its purpose and objectives, its benefits, as well as its risks. Institutions must be responsible for decisions made by an AI system.
  6. Accuracy, Reliability, and Validity Obligations. Institutions must ensure the accuracy, reliability, and validity of decisions.
  7. Data Quality Obligation. Institutions must establish data provenance, and assure quality and relevance for the data input into algorithms.
  8. Public Safety Obligation. Institutions must assess the public safety risks that arise from the deployment of AI systems that direct or control physical devices, and implement safety controls.
  9. Cybersecurity Obligation. Institutions must secure AI systems against cybersecurity threats.
  10. Prohibition on Secret Profiling. No institution shall establish or maintain a secret profiling system.
  11. Prohibition on Unitary Scoring. No national government shall establish or maintain a general-purpose score on its citizens or residents.
  12. Termination Obligation. An institution that has established an AI system has an affirmative obligation to terminate the system if human control of the system is no longer possible.

References

ACM (2017) 'Statement on Algorithmic Transparency and Accountability' Association for Computing Machinery, January 2017, at https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement_algorithms.pdf

Asimov I. (1942) 'Runaround' (originally published in 1942), reprinted in Asimov I. 'I, Robot' Grafton Books, London, 1968, pp. 33- 51

Bolter J.D. (1986) 'Turing's Man: Western Culture in the Computer Age' The North Carolina University Press, 1984; Pelican, 1986

BS (2016) 'Robots and robotic devices. Guide to the ethical design and application of robots and robotic systems' British Standards Institute, 2016

Clarke R. (1989) 'Knowledge-Based Expert Systems: Risk Factors and Potentially Profitable Application Area', Xamax Consultancy Pty Ltd, January 1989, at http://www.rogerclarke.com/SOS/KBTE.html

Clarke R. (1993-94) 'Asimov's Laws of Robotics: Implications for Information Technology' In two parts, in IEEE Computer 26,12 (December 1993) 53-61, and 27,1 (January 1994) 57-66, at http://www.rogerclarke.com/SOS/Asimov.html

Clarke R. (2005) 'Human-Artefact Hybridisation: Forms and Consequences' Proc. Ars Electronica 2005 Symposium on Hybrid - Living in Paradox, Linz, Austria, 2-3 September 2005, PrePrint at http://www.rogerclarke.com/SOS/HAH0505.html

CLA-EP (2016) 'Recommendations on Civil Law Rules on Robotics' Committee on Legal Affairs of the European Parliament, 31 May 2016, at http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//NONSGML%2BCOMPARL%2BPE-582.443%2B01%2BDOC%2BPDF%2BV0//EN

Devlin H. (2016). 'Do no harm, don't discriminate: official guidance issued on robot ethics' The Guardian, 18 Sep 2016, at https://www.theguardian.com/technology/2016/sep/18/official-guidance-robot-ethics-british-standards-institute

FLI (2017) 'Asilomar AI Principles' Future of Life Institute, January 2017, at https://futureoflife.org/ai-principles/?cn-reloaded=1

GEFA (2016) 'Position on Robotics and AI' The Greens / European Free Alliance Digital Working Group, November 2016, at https://juliareda.eu/wp-content/uploads/2017/02/Green-Digital-Working-Group-Position-on-Robotics-and-Artificial-Intelligence-2016-11-22.pdf

Google (2018) 'Objectives for AI applications' Google, June 2018, at https://www.blog.google/technology/ai/ai-principles/

Hirano (2017) 'AI R&D guidelines' Proc. OECD Conf. on AI developments and applications, October 2017, http://www.oecd.org/going-digital/ai-intelligent-machines-smart-policies/conference-agenda/ai-intelligent-machines-smart-policies-hirano.pdf

HOL (2018) 'AI in the UK: ready, willing and able?' Select Committee on Artificial Intelligence, House of Lords, April 2018, at https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf

ICO (2017) 'Big data, artificial intelligence, machine learning and data protection' UK Information Commissioner's Office, Discussion Paper v.2.2, September 2017, at https://ico.org.uk/for-organisations/guide-to-data-protection/big-data/

IEEE (2017) 'Ethically Aligned Design', Version 2. IEEE, December 2017. at http://standards.ieee.org/develop/indconn/ec/autonomous_systems.html

ISOC (2017) 'Artificial Intelligence and Machine Learning: Policy Paper' Internet Society, April 2017, at https://www.internetsociety.org/resources/doc/2017/artificial-intelligence-and-machine-learning-policy-paper/

ITIC (2017) 'AI Policy Principles' Information Technology Industry Council, undated but apparently of October 2017, at https://www.itic.org/resources/AI-Policy-Principles-FullReport2.pdf

MS (2018) 'Microsoft AI principles' Microsoft, August 2018, at https://www.microsoft.com/en-us/ai/our-approach-to-ai

Newcomer E. (2018). 'What Google's AI Principles Left Out: We're in a golden age for hollow corporate statements sold as high-minded ethical treatises' Bloomberg, 8 June 2018, at https://www.bloomberg.com/news/articles/2018-06-08/what-google-s-ai-principles-left-out

Pichai S. (2018) 'AI at Google: our principles' Google Blog, 7 Jun 2018, at https://www.blog.google/technology/ai/ai-principles/

PoAI (2018) 'Our Work (Thematic Pillars)' Partnership on AI, April 2018, at https://www.partnershiponai.org/about/#pillar-1

Rayome A.D. (2017) 'Guiding principles for ethical AI, from IBM CEO Ginni Rometty', TechRepublic (17 January 2017), at https://www.techrepublic.com/article/3-guiding-principles-for- ethical-ai-from-ibm-ceo-ginni-rometty/

Smith R. (2018). '5 core principles to keep AI ethical'. World Economic Forum, 19 Apr 2018, at https://www.weforum.org/agenda/2018/04/keep-calm-and-make-ai-ethical/

TPV (2018) 'Universal Guidelines for Artificial Intelligence' The Public Voice, October 2018, at https://thepublicvoice.org/ai-universal-guidelines/

UGU (2017) 'Top 10 Principles for Ethical AI' UNI Global Union, December 2017, at http://www.thefutureworldofwork.org/media/35420/uni_ethical_ai.pdf

Wyndham J. (1932) 'The Lost Machine' (originally published in 1932), reprinted in A. Wells (Ed.) 'The Best of John Wyndham' Sphere Books, London, 1973, pp. 13- 36, and in Asimov I., Warrick P.S. & Greenberg M.H. (Eds.) 'Machines That Think' Holt, Rinehart, and Wilson, 1983, pp. 29-49


Author Affiliations

Roger Clarke is Principal of Xamax Consultancy Pty Ltd, Canberra. He is also a Visiting Professor in Cyberspace Law & Policy at the University of N.S.W., and a Visiting Professor in the Research School of Computer Science at the Australian National University. He has also spent many years on the Board of the Australian Privacy Foundation, and is Company Secretary of the Internet Society of Australia.



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