Awareness of Misuse

Designers of A IS creators should consider and guard against potential misuses and operational risks
Principle: Ethical Aspects of Autonomous and Intelligent Systems, Jun 24, 2019

Published by The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems

Related Principles

(c) Responsibility

The principle of responsibility must be fundamental to AI research and application. ‘Autonomous’ systems should only be developed and used in ways that serve the global social and environmental good, as determined by outcomes of deliberative democratic processes. This implies that they should be designed so that their effects align with a plurality of fundamental human values and rights. As the potential misuse of ‘autonomous’ technologies poses a major challenge, risk awareness and a precautionary approach are crucial. Applications of AI and robotics should not pose unacceptable risks of harm to human beings, and not compromise human freedom and autonomy by illegitimately and surreptitiously reducing options for and knowledge of citizens. They should be geared instead in their development and use towards augmenting access to knowledge and access to opportunities for individuals. Research, design and development of AI, robotics and ‘autonomous’ systems should be guided by an authentic concern for research ethics, social accountability of developers, and global academic cooperation to protect fundamental rights and values and aim at designing technologies that support these, and not detract from them.

Published by European Group on Ethics in Science and New Technologies, European Commission in Ethical principles and democratic prerequisites, Mar 9, 2018

3. Principle 3 — Accountability

Issue: How can we assure that designers, manufacturers, owners, and operators of A IS are responsible and accountable? [Candidate Recommendations] To best address issues of responsibility and 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 because A IS oriented technology and their impacts are too new (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: • Intended use • 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

Published by The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems in Ethically Aligned Design (v2): General Principles, (v1) Dec 13, 2016. (v2) Dec 12, 2017

5. Principle 5 — A IS Technology Misuse and Awareness of It

Issue: How can we extend the benefits and minimize the risks of A IS technology being misused? [Candidate Recommendations] Raise public awareness around the issues of potential A IS technology misuse in an informed and measured way by: 1. Providing ethics education and security awareness that sensitizes society to the potential risks of misuse of A IS (e.g., by providing “data privacy” warnings that some smart devices will collect their user’s personal data). 2. Delivering this education in scalable and effective ways, beginning with those having the greatest credibility and impact that also minimize generalized (e.g., non productive) fear about A IS (e.g., via credible research institutions or think tanks via social media such as Facebook or YouTube). 3. Educating government, lawmakers, and enforcement agencies surrounding these issues so citizens work collaboratively with them to avoid fear or confusion (e.g., in the same way police officers have given public safety lectures in schools for years; in the near future they could provide workshops on safe A IS).

Published by The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems in Ethically Aligned Design (v2): General Principles, (v1) Dec 13, 2016. (v2) Dec 12, 2017

4. Risk Assessment and Management

Regulatory and non regulatory approaches to AI should be based on a consistent application of risk assessment and risk management across various agencies and various technologies. It is not necessary to mitigate every foreseeable risk; in fact, a foundational principle of regulatory policy is that all activities involve tradeoffs. Instead, a risk based approach should be used to determine which risks are acceptable and which risks present the possibility of unacceptable harm, or harm that has expected costs greater than expected benefits. Agencies should be transparent about their evaluations of risk and re evaluate their assumptions and conclusions at appropriate intervals so as to foster accountability. Correspondingly, the magnitude and nature of the consequences should an AI tool fail, or for that matter succeed, can help inform the level and type of regulatory effort that is appropriate to identify and mitigate risks. Specifically, agencies should follow the direction in Executive Order 12866, “Regulatory Planning and Review,”to consider the degree and nature of the risks posed by various activities within their jurisdiction. Such an approach will, where appropriate, avoid hazard based and unnecessarily precautionary approaches to regulation that could unjustifiably inhibit innovation.

Published by The White House Office of Science and Technology Policy (OSTP), United States in Principles for the Stewardship of AI Applications, Jan 13, 2020

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.

Published by ACM US Public Policy Council (USACM) in Principles for Algorithmic Transparency and Accountability, Jan 12, 2017