USA releases proposed guidance for regulation of AI applications

The United States Office of Science and Technology Policy has released draft guidance for regulation that it proposes agencies must adhere to when drawing up new AI regulations for the private sector. The document includes 10 “Principles for the Stewardship of AI Applications”. These principles have three main goals: to ensure public engagement, limit regulatory overreach, and promote trustworthy technology.

The 10 principles, along with the accompanying explanations in the document, are as follows:

1. Public trust in AI. AI is expected to have a positive impact across sectors of social and economic life, including employment, transportation, education, finance, healthcare, personal security, and manufacturing. At the same time, AI applications could pose risks to privacy, individual rights, autonomy, and civil liberties that must be carefully assessed and appropriately addressed. Its continued adoption and acceptance will depend significantly on public trust and validation. It is therefore important that the government’s regulatory and non-regulatory approaches to AI promote reliable, robust, and trustworthy AI applications, which will contribute to public trust in AI. The appropriate regulatory or non-regulatory response to privacy and other risks must necessarily depend on the nature of the risk presented and the appropriate mitigations.
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 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.
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.
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.
5. Benefits and costs. When developing regulatory and non-regulatory approaches, agencies will often consider the application and deployment of AI into already-regulated industries. Presumably, such significant investments would not occur unless they offered significant economic potential. As in all technological transitions of this nature, the introduction of AI may also create unique challenges. For example, while the broader legal environment already applies to AI applications, the application of existing law to questions of responsibility and liability for decisions made by AI could be unclear in some instances, leading to the need for agencies, consistent with their authorities, to evaluate the benefits, costs, and distributional effects associated with any identified or expected method for accountability. Executive Order 12866 calls on agencies to “select those approaches that maximize net benefits (including potential economic, environmental, public health and safety, and other advantages; distributive impacts; and equity).” Agencies should, when consistent with law, carefully consider the full societal costs, benefits, and distributional effects before considering regulations related to the development and deployment of AI applications. Such consideration will include the potential benefits and costs of employing AI, when compared to the systems AI has been designed to complement or replace, whether implementing AI will change the type of errors created by the system, as well as comparison to the degree of risk tolerated in other existing ones. Agencies should also consider critical dependencies when evaluating AI costs and benefits, as technological factors (such as data quality) and changes in human processes associated with AI implementation may alter the nature and magnitude of the risks and benefits. In cases where a comparison to a current system or process is not available, evaluation of risks and costs of not implementing the system should be evaluated as well.
6. Flexibility. When developing regulatory and non-regulatory approaches, agencies should pursue performance-based and flexible approaches that can adapt to rapid changes and updates to AI applications. Rigid, design-based regulations that attempt to prescribe the technical specifications of AI applications will in most cases be impractical and ineffective, given the anticipated pace with which AI will evolve and the resulting need for agencies to react to new information and evidence. Targeted agency conformity assessment schemes, to protect health and safety, privacy, and other values, will be essential to a successful, and flexible, performance-based approach. To advance American innovation, agencies should keep in mind international uses of AI, ensuring that American companies are not disadvantaged by the United States’ regulatory regime.
7. Fairness and non-discrimination. Agencies should consider in a transparent manner the impacts that AI applications may have on discrimination. AI applications have the potential of reducing present-day discrimination caused by human subjectivity. At the same time, applications can, in some instances, introduce real-world bias that produces discriminatory outcomes or decisions that undermine public trust and confidence in AI. When considering regulations or non-regulatory approaches related to AI applications, agencies should consider, in accordance with law, issues of fairness and non-discrimination with respect to outcomes and decisions produced by the AI application at issue, as well as whether the AI application at issue may reduce levels of unlawful, unfair, or otherwise unintended discrimination as compared to existing processes.
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.
9. Safety and security. Agencies should promote the development of AI systems that are safe, secure, and operate as intended, and encourage the consideration of safety and security issues throughout the AI design, development, deployment, and operation process. Agencies should pay particular attention to the controls in place to ensure the confidentiality, integrity, and availability of the information processed, stored, and transmitted by AI systems. Agencies should give additional consideration to methods for guaranteeing systemic resilience, and for preventing bad actors from exploiting AI system weaknesses, including cybersecurity risks posed by AI operation, and adversarial use of AI against a regulated entity’s AI technology. When evaluating or introducing AI policies, agencies should be mindful of any potential safety and security risks, as well as the risk of possible malicious deployment and use of AI applications.
10. Interagency coordination. A coherent and whole-of-government approach to AI oversight requires interagency coordination. Agencies should coordinate with each other to share experiences and to ensure consistency and predictability of AI-related policies that advance American innovation and growth in AI, while appropriately protecting privacy, civil liberties, and American values and allowing for sector- and application-specific approaches when appropriate. When OMB’s Office of Information and Regulatory Affairs (OIRA) designates AI-related draft regulatory action as “significant” for purposes of interagency review under Executive Order 12866, OIRA will ensure that all agencies potentially affected by or interested in a particular action will have an opportunity to provide input.

The proposed regulations form the latest step in the American AI Initiative, which was launched early last year with the aim of creating a national strategy for AI. At present the regulatory guidance is in draft form and interested parties have until 13 March 2020 to submit their comments via the Federal Register.

Read the full document outlining the regulatory guidelines here.

Lucy Smith is Senior Managing Editor for AIhub.

Lucy Smith is Senior Managing Editor for AIhub.