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

Published by ACM US Public Policy Council (USACM)

Related Principles

Fairness

Throughout their lifecycle, AI systems should be inclusive and accessible, and should not involve or result in unfair discrimination against individuals, communities or groups. This principle aims to ensure that AI systems are fair and that they enable inclusion throughout their entire lifecycle. AI systems should be user centric and designed in a way that allows all people interacting with it to access the related products or services. This includes both appropriate consultation with stakeholders, who may be affected by the AI system throughout its lifecycle, and ensuring people receive equitable access and treatment. This is particularly important given concerns about the potential for AI to perpetuate societal injustices and have a disparate impact on vulnerable and underrepresented groups including, but not limited to, groups relating to age, disability, race, sex, intersex status, gender identity and sexual orientation. Measures should be taken to ensure the AI produced decisions are compliant with anti‐discrimination laws.

Published by Department of Industry, Innovation and Science, Australian Government in AI Ethics Principles, Nov 7, 2019

· Informed consent

Measures should be taken to ensure that stakeholders of AI systems are with sufficient informed consent about the impact of the system on their rights and interests. When unexpected circumstances occur, reasonable data and service revocation mechanisms should be established to ensure that users' own rights and interests are not infringed.

Published by Beijing Academy of Artificial Intelligence (BAAI); Peking University; Tsinghua University; Institute of Automation, Chinese Academy of Sciences; Institute of Computing Technology, Chinese Academy of Sciences; Artifical Intelligence Industry Innovation Strategy Alliance (AITISA); etc. in Beijing AI Principles, May 25, 2019

• 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

Ensuring Accountability

Principle: Legal accountability has to be ensured when human agency is replaced by decisions of AI agents. Recommendations: Ensure legal certainty: Governments should ensure legal certainty on how existing laws and policies apply to algorithmic decision making and the use of autonomous systems to ensure a predictable legal environment. This includes working with experts from all disciplines to identify potential gaps and run legal scenarios. Similarly, those designing and using AI should be in compliance with existing legal frameworks. Put users first: Policymakers need to ensure that any laws applicable to AI systems and their use put users’ interests at the center. This must include the ability for users to challenge autonomous decisions that adversely affect their interests. Assign liability up front: Governments working with all stakeholders need to make some difficult decisions now about who will be liable in the event that something goes wrong with an AI system, and how any harm suffered will be remedied.

Published by Internet Society, "Artificial Intelligence and Machine Learning: Policy Paper" in Guiding Principles and Recommendations, Apr 18, 2017

5 DEMOCRATIC PARTICIPATION PRINCIPLE

AIS must meet intelligibility, justifiability, and accessibility criteria, and must be subjected to democratic scrutiny, debate, and control. 1) AIS processes that make decisions affecting a person’s life, quality of life, or reputation must be intelligible to their creators. 2) The decisions made by AIS affecting a person’s life, quality of life, or reputation should always be justifiable in a language that is understood by the people who use them or who are subjected to the consequences of their use. Justification consists in making transparent the most important factors and parameters shaping the decision, and should take the same form as the justification we would demand of a human making the same kind of decision. 3) The code for algorithms, whether public or private, must always be accessible to the relevant public authorities and stakeholders for verification and control purposes. 4) The discovery of AIS operating errors, unexpected or undesirable effects, security breaches, and data leaks must imperatively be reported to the relevant public authorities, stakeholders, and those affected by the situation. 5) In accordance with the transparency requirement for public decisions, the code for decision making algorithms used by public authorities must be accessible to all, with the exception of algorithms that present a high risk of serious danger if misused. 6) For public AIS that have a significant impact on the life of citizens, citizens should have the opportunity and skills to deliberate on the social parameters of these AIS, their objectives, and the limits of their use. 7) We must at all times be able to verify that AIS are doing what they were programmed for and what they are used for. 8) Any person using a service should know if a decision concerning them or affecting them was made by an AIS. 9) Any user of a service employing chatbots should be able to easily identify whether they are interacting with an AIS or a real person. 10) Artificial intelligence research should remain open and accessible to all.

Published by University of Montreal in The Montreal Declaration for a Responsible Development of Artificial Intelligence, Dec 4, 2018