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.
Principle: Principles for Algorithmic Transparency and Accountability, Jan 12, 2017

Published by ACM US Public Policy Council (USACM)

Related Principles

· 2. The Principle of Non maleficence: “Do no Harm”

AI systems should not harm human beings. By design, AI systems should protect the dignity, integrity, liberty, privacy, safety, and security of human beings in society and at work. AI systems should not threaten the democratic process, freedom of expression, freedoms of identify, or the possibility to refuse AI services. At the very least, AI systems should not be designed in a way that enhances existing harms or creates new harms for individuals. Harms can be physical, psychological, financial or social. AI specific harms may stem from the treatment of data on individuals (i.e. how it is collected, stored, used, etc.). To avoid harm, data collected and used for training of AI algorithms must be done in a way that avoids discrimination, manipulation, or negative profiling. Of equal importance, AI systems should be developed and implemented in a way that protects societies from ideological polarization and algorithmic determinism. Vulnerable demographics (e.g. children, minorities, disabled persons, elderly persons, or immigrants) should receive greater attention to the prevention of harm, given their unique status in society. Inclusion and diversity are key ingredients for the prevention of harm to ensure suitability of these systems across cultures, genders, ages, life choices, etc. Therefore not only should AI be designed with the impact on various vulnerable demographics in mind but the above mentioned demographics should have a place in the design process (rather through testing, validating, or other). Avoiding harm may also be viewed in terms of harm to the environment and animals, thus the development of environmentally friendly AI may be considered part of the principle of avoiding harm. The Earth’s resources can be valued in and of themselves or as a resource for humans to consume. In either case it is necessary to ensure that the research, development, and use of AI are done with an eye towards environmental awareness.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

2. Transparency

For cognitive systems to fulfill their world changing potential, it is vital that people have confidence in their recommendations, judgments and uses. Therefore, the IBM company will make clear: When and for what purposes AI is being applied in the cognitive solutions we develop and deploy. The major sources of data and expertise that inform the insights of cognitive solutions, as well as the methods used to train those systems and solutions. The principle that clients own their own business models and intellectual property and that they can use AI and cognitive systems to enhance the advantages they have built, often through years of experience. We will work with our clients to protect their data and insights, and will encourage our clients, partners and industry colleagues to adopt similar practices.

Published by IBM in Principles for the Cognitive Era, Jan 17, 2017

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

· 1.4 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 finding 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.

Published by Information Technology Industry Council (ITI) in AI Policy Principles, Oct 24, 2017

• 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. [Recommendations] • Standing for “Accountable Artificial Intelligence”: Governments, industry and academia should apply the Information Accountability Foundation’s principles to AI. Organizations implementing AI solutions should be able to demonstrate to regulators that they have the right processes, policies and resources in place to meet those principles. • Transparent decisions: Governments should determine which AI implementations require algorithm explainability to mitigate discrimination and harm to individuals.

Published by Intel in AI public policy principles, Oct 18, 2017