2. Public Participation

Public participation, especially in those instances where AI uses information about individuals, will improve agency accountability and regulatory outcomes, as well as increase public trust and confidence. Agencies should provide ample opportunities for the public to national standard for a specific aspect related to AI is not essential, however, agencies should provide information and participate in all stages of the rulemaking process, to the extent feasible and consistent with legal requirements (including legal constraints on participation in certain situations, for example, national security preventing imminent threat to or responding to emergencies). Agencies are also encouraged, to the extent practicable, to inform the public and promote awareness and widespread availability of standards and the creation of other informative documents.
Principle: Principles for the Stewardship of AI Applications, Jan 13, 2020

Published by The White House Office of Science and Technology Policy (OSTP), United States

Related Principles

Transparency and explainability

There should be transparency and responsible disclosure to ensure people know when they are being significantly impacted by an AI system, and can find out when an AI system is engaging with them. This principle aims to ensure responsible disclosure when an AI system is significantly impacting on a person’s life. The definition of the threshold for ‘significant impact’ will depend on the context, impact and application of the AI system in question. Achieving transparency in AI systems through responsible disclosure is important to each stakeholder group for the following reasons for users, what the system is doing and why for creators, including those undertaking the validation and certification of AI, the systems’ processes and input data for those deploying and operating the system, to understand processes and input data for an accident investigator, if accidents occur for regulators in the context of investigations for those in the legal process, to inform evidence and decision‐making for the public, to build confidence in the technology Responsible disclosures should be provided in a timely manner, and provide reasonable justifications for AI systems outcomes. This includes information that helps people understand outcomes, like key factors used in decision making. This principle also aims to ensure people have the ability to find out when an AI system is engaging with them (regardless of the level of impact), and are able to obtain a reasonable disclosure regarding the AI system.

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

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

8 PRUDENCE PRINCIPLE

Every person involved in AI development must exercise caution by anticipating, as far as possible, the adverse consequences of AIS use and by taking the appropriate measures to avoid them. 1) It is necessary to develop mechanisms that consider the potential for the double use — beneficial and harmful —of AI research and AIS development (whether public or private) in order to limit harmful uses. 2) When the misuse of an AIS endangers public health or safety and has a high probability of occurrence, it is prudent to restrict open access and public dissemination to its algorithm. 3) Before being placed on the market and whether they are offered for charge or for free, AIS must meet strict reliability, security, and integrity requirements and be subjected to tests that do not put people’s lives in danger, harm their quality of life, or negatively impact their reputation or psychological integrity. These tests must be open to the relevant public authorities and stakeholders. 4) The development of AIS must preempt the risks of user data misuse and protect the integrity and confidentiality of personal data. 5) The errors and flaws discovered in AIS and SAAD should be publicly shared, on a global scale, by public institutions and businesses in sectors that pose a significant danger to personal integrity and social organization.

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

3. Scientific Integrity and Information Quality

The government’s regulatory and non regulatory approaches to AI applications should leverage scientific and technical information and processes. Agencies should hold information, whether produced by the government or acquired by the government from third parties, that is likely to have a clear and substantial influence on important public policy or private sector decisions (including those made by consumers) to a high standard of quality, transparency, and compliance. Consistent with the principles of scientific integrity in the rulemaking and guidance processes, agencies should develop regulatory approaches to AI in a manner that both informs policy decisions and fosters public trust in AI. Best practices include transparently articulating the strengths, weaknesses, intended optimizations or outcomes, bias mitigation, and appropriate uses of the AI application’s results. Agencies should also be mindful that, for AI applications to produce predictable, reliable, and optimized outcomes, data used to train the AI system must be of sufficient quality for the intended use.

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

8. Disclosure and Transparency

In addition to improving the rulemaking process, transparency and disclosure can increase public trust and confidence in AI applications. At times, such disclosures may include identifying when AI is in use, for instance, if appropriate for addressing questions about how the application impacts human end users. Agencies should be aware that some applications of AI could increase human autonomy. Agencies should carefully consider the sufficiency of existing or evolving legal, policy, and regulatory environments before contemplating additional measures for disclosure and transparency. What constitutes appropriate disclosure and transparency is context specific, depending on assessments of potential harms, the magnitude of those harms, the technical state of the art, and the potential benefits of the AI application.

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