While scores of commentators have opined about the need for governance of artificial intelligence (AI), fewer have examined the implications for government itself. This chapter offers a synthetic review of an emerging literature on the distinct governance challenges raised by public sector adoption of AI.
Section 2 begins by providing a sense of the landscape of government AI use. While existing work centers on a few use cases (e.g., criminal risk assessment scores), a new wave of AI technology is exhibiting early signs of transforming how government works. Such AI-based governance technologies cover the waterfront of government action, from securities enforcement and patent classification to social security disability benefits adjudication and environmental monitoring. We show how these new algorithmic tools differ from past rounds of public sector innovation and raise unique governance challenges. We highlight three such challenges emerging from the literature.
Section 3 reviews the legal challenges of reconciling public law’s commitment to reason-giving with the lack of explainability of certain algorithmic governance tools. Because existing work has fixated on a small set of uses, it reflects the tendency in the wider algorithmic accountability literature to focus on constitutional doctrine. But the diverse set of algorithmic governance tools coming online are more likely to be regulated under statutory administrative law, raising distinct questions about transparency and explainability. Next, Section 4 reviews the challenges of building state capacity to adopt modern AI tools. We argue that a core component of state capacity includes embedded technical expertise and data infrastructure. Standard frameworks fail to capture how capacity-building can be critical for (a) shrinking the public-private sector technology gap and (b) “internal” due process, which administrative law has increasingly recognized as key to accountability. Finally, Section 5 turns to challenges of gameability, distributive effects, and legitimacy as the new AI-based governance technologies move closer to performing core government functions. We highlight the potential for adversarial learning by regulated parties and contractor conflicts of interest when algorithms are bought, not made. Gaming concerns highlight the deeper political complexities of a newly digitized public sector.
Section 6 concludes by providing cautious support for adoption of AI by the public sector. Further progress in thinking about the new algorithmic governance will require more sustained attention to the legal and institutional realities and technological viability of use cases.