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Abstract Public decision-makers incorporate algorithm decision aids, often developed by private businesses, into the policy process, in part, as a method for justifying difficult decisions. Ethicists have worried that over-trust in algorithm advice and concerns about punishment if departing from an algorithm’s recommendation will result in over-reliance and harm democratic accountability. We test these concerns in a set of two pre-registered survey experiments in the judicial context conducted on three representative U.S. samples. The results show no support for the hypothesized blame dynamics, regardless of whether the judge agrees or disagrees with the algorithm. Algorithms, moreover, do not have a significant impact relative to other sources of advice. Respondents who are generally more trusting of elites assign greater blame to the decision-maker when they disagree with the algorithm, and they assign more blame when they think the decision-maker is abdicating their responsibility by agreeing with an algorithm. 

Abstract The use of algorithms and automated systems, especially those leveraging artificial intelligence (AI), has been exploding in the public sector, but their use has been controversial. Ethicists, public advocates, and legal scholars have debated whether biases in AI systems should bar their use or if the potential net benefits, especially toward traditionally disadvantaged groups, justify even greater expansion. While this debate has become voluminous, no scholars of which we are aware have conducted experiments with the groups affected by these policies about how they view the trade-offs. We conduct a set of two conjoint experiments with a high-quality sample of 973 Americans who identify as Black or African American in which we randomize the levels of inter-group disparity in outcomes and the net effect on such adverse outcomes in two highly controversial contexts: pre-trial detention and traffic camera ticketing. The results suggest that respondents are willing to tolerate some level of disparity in outcomes in exchange for certain net improvements for their community. These results turn this debate from an abstract ethical argument into an evaluation of political feasibility and policy design based on empirics. 

This essay compares the claims of trade secret privilege in criminal proceedings asserted by the creators of AI systems with the assertions of such privilege in patent infringement discovery. This comparison reveals that, in the case of patent infringement, courts have generally limited such privilege assertions. This essay argues that the case for discovery in the criminal context should be stronger, rather than weaker, in the criminal context. 

The foundations of Artificial Intelligence (AI), a field whose applications are of great use and concern for society, can be traced back to the early years of the second half of the 20th century. Since then, the field has seen increased research output and funding cycles followed by setbacks. The new millennium has seen unprecedented interest in AI progress and expectations with significant financial investments from the public and private sectors. However, the continual acceleration of AI capabilities and real-world applications is not guaranteed. Mainly, accountability of AI systems in the context of the interplay between AI and the broader society is essential for adopting AI systems via the trust placed in them. Continual progress in AI research and development (R&D) can help tackle humanity's most significant challenges to improve social good. The authors of this paper suggest that the careful design of forward-looking research policies serves a crucial function in avoiding potential future setbacks in AI research, development, and use. The United States (US) has kept its leading role in R&D, mainly shaping the global trends in the field. Accordingly, this paper presents a critical assessment of the US National AI R&D Strategic Plan and prescribes six recommendations to improve future research strategies in the US and around the globe.