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Federal Register / Vol. 86, No. 13 / Friday, January 22, 2021 / Notices
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transparent. For instance, depending on the nature of their operations, agencies might prioritize transparency to the public, courts, Congress, or their own officials.
The appropriate level or nature of transparency and interpretability in agencies AI systems will also depend on context. In some contexts, such as adjudication, reasongiving requirements may call for a higher degree of transparency and interpretability from agencies regarding how their AI systems function. In other contexts, such as enforcement, agencies legitimate interests in preventing gaming or adversarial learning by regulated parties could militate against providing too much information or specific types of information to the public about AI
systems processes. In every context, agencies should consider whether particular laws or policies governing disclosure of information apply.
In selecting and using AI techniques, agencies should be cognizant of the degree to which a particular AI system can be made transparent to appropriate people and entities, including the general public. There may be tradeoffs between explainability and accuracy in AI systems, so that transparency and interpretability might sometimes weigh in favor of choosing simpler AI models. The appropriate balance between explainability and accuracy will depend on the specific context, including agencies circumstances and priorities.
The proprietary nature of some AI systems may also affect the extent to which they can be made transparent. When agencies AI
systems rely on proprietary technologies or algorithms the agencies do not own, the agencies and the public may have limited access to the information about the AI
techniques. Agencies should strive to anticipate such circumstances and address them appropriately, such as by working with outside providers to ensure they will be able to share sufficient information about such a system. Agencies should not enter into contracts to use proprietary AI systems unless they are confident that actors both internal and external to the agencies will have adequate access to information about the systems.
2. Harmful Bias At their best, AI systems can help agencies identify and reduce the impact of harmful biases.4 Yet they can also unintentionally create or exacerbate those biases by encoding and deploying them at scale. In deciding whether and how to deploy an AI system, agencies should carefully evaluate the harmful biases that might result from the use of the AI system as well as the biases that might result from alternative systems such as an incumbent system that the AI system would augment or replace. Because different types of bias pose different types of harms, the outcome of the evaluation will depend on agencies unique circumstances and priorities and the consequences posed by those harms in those contexts.
4 While the term bias has a technical, statistical meaning, the Administrative Conference here uses the term more generally, to refer to common or systematic errors in decision making.

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AI systems can be biased because of their reliance on data reflecting historical human biases or because of their designs. Biases in AI systems can increase over time through feedback. That can occur, for example, if the use of a biased AI system leads to systematic errors in categorizations, which are then reflected in the data set or data environment the system uses to make future predictions.
Agencies should be mindful of the interdependence of the models, metrics, and data that underpin AI systems.
Identifying harmful biases in AI systems can pose challenges. To identify and mitigate biases, agencies should, to the extent practical, consider whether other data or methods are available. Agencies should periodically examine and refresh AI
algorithms and other protocols to ensure that they remain sufficiently current and reflect new information and circumstances relevant to the functions they perform.
Data science techniques for identifying and mitigating harmful biases in AI systems are developing. Agencies should stay up to date on developments in the field of AI, particularly on algorithmic fairness; establish processes to ensure that personnel that reflect various disciplines and relevant perspectives are able to inspect AI systems and their decisions for indications of harmful bias; test AI systems in environments resembling the ones in which they will be used; and make use of internal and external processes for evaluating the risks of harmful bias in AI
systems and for identifying such bias.
3. Technical Capacity AI systems can help agencies conserve resources, but they can also require substantial investments of human and financial capital. Agencies should carefully evaluate the shortand long-term costs and benefits of an AI system before committing significant resources to it. Agencies should also ensure they have access to the technical expertise required to make informed decisions about the type of AI systems they require; how to integrate those systems into their operations; and how to oversee, maintain, and update those systems.
Given the data science fields ongoing and rapid development, agencies should consider cultivating an AI-ready workforce, including through recruitment and training efforts that emphasize AI skills. When agency personnel lack the skills to develop, procure, or maintain AI systems that meet agencies needs, agencies should consider other means of expanding their technical expertise, including by relying on tools such as the Intergovernmental Personnel Act,5 prize competitions, cooperative research and development agreements with private institutions or universities, and consultation with external technical advisors and subjectmatter experts.
4. Obtaining AI Systems Decisions about whether to obtain an AI
system can involve important trade-offs.
Obtaining AI systems from external sources might allow agencies to acquire more sophisticated tools than they could design on 55

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their own, access those tools sooner, and save some of the up-front costs associated with developing the technical capacity needed to design AI systems.6 Creating AI tools within agencies, by contrast, might yield tools that are better tailored to the agencies particular tasks and policy goals. Creating AI systems within agencies can also facilitate development of internal technical capability, which can yield benefits over the lifetime of the AI systems and in other technological tasks the agencies may confront.
Certain government offices are available to help agencies with decisions and actions related to technology.7 Agencies should make appropriate use of these resources when obtaining an AI system. Agencies should also consider the cost and availability of the technical support necessary to ensure that an AI system can be maintained and updated in a manner consistent with its expected life cycle and service mission.
5. Data AI systems require data, often in vast quantities. Agencies should consider whether they have, or can obtain, data that appropriately reflect conditions similar to the ones the agencies AI systems will address in practice; whether the agencies have the resources to render the data into a format that can be used by the agencies AI systems; and how the agencies will maintain the data and link them to their AI systems without compromising security or privacy. Agencies should also review and consider statutes and regulations that impact their uses of AI as a potential collector and consumer of data.8
6. Privacy Agencies have a responsibility to protect privacy with respect to personally identifiable information in AI systems. In a narrow sense, this responsibility demands that agencies comply with requirements related to, for instance, transparency, due process, accountability, and information quality and integrity established by the Privacy Act of 1974, Section 208 of the E-Government Act of 2002, and other applicable laws and policies.9 More broadly, agencies should recognize and appropriately manage privacy risks posed by an AI system.
Agencies should consider privacy risks 6 Agencies may also obtain AI systems that are embedded in commercial products. The considerations applicable to such embedded AI
systems should reflect the fact that agencies may have less control over their design and development.
7 Within the General Services Administration, for example, the office called 18F routinely partners with government agencies to help them build and buy technologies. Similarly, the United States Digital Service which is within the Executive Office of the President has a staff of technologists whose job is to help agencies build better technological tools. While the two entities have different approaches18F acts more like an information intermediary and the Digital Service serves as an alternative source for information technology contractsboth could aid agencies with obtaining, developing, and using different AI
techniques.
8 See, e.g., Paperwork Reduction Act, 44 U.S.C.
350120.
9 See, e.g., 5 U.S.C. 552ae, g, & p; 44 U.S.C.
3501 note.

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