In the context of rising delegation of administrative discretion to advanced technologies, this study aims to quantitatively assess key public values that may be at risk when governments employ automated decision systems (ADS).Drawing on the public value failure framework coupled with experimental methodology, we address the need to measure and compare the salience of three such values-fairness, transparency, and human responsiveness. Based on a preregistered design, we administer a survey experiment to 1460 American adults inspired by prominent ADS applications in child welfare and criminal justice. The results provide clear causal evidence that certain public value failures associated with artificial intelligence have significant negative impacts on citizens' evaluations of government. We find substantial negative citizen reactions when fairness and transparency are not realized in the implementation of ADS.These results transcend both policy context and political ideology and persist even when respondents are not themselves personally impacted. | INTRODUCTIONGovernments have a special responsibility to realize key values held by the public. Yet scholars of public value failure recognize that this core responsibility may not be satisfied when administrators overemphasize efficiency and other economic goals at the expense of publicly-held values such as equity, transparency, and responsiveness (Bozeman, 2002;Jørgensen & Bozeman, 2007). In this study, we examine public value failure in the context of a phenomenon of increasing importance: the delegation of discretion to automated decision systems (ADS), or artificial intelligence (AI)-based tools that provide predictions and recommendations to government officials. In particular, the adoption of AI by governments to enhance efficiency in service provision may result in supplanting or otherwise

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This study addresses the phenomenon of misinformation about misinformation, or politicians "crying wolf" over fake news. Strategic and false allegations that stories are fake news or deepfakes may benefit politicians by helping them maintain support in the face of information damaging to their reputation. We posit that this concept, known as the "liar's dividend," works through two theoretical channels: by invoking informational uncertainty or by encouraging oppositional rallying of core supporters. To evaluate the implications of the liar's dividend, we use three survey experiments detailing hypothetical politician responses to video or text news stories depicting real politician scandals. We find that allegations of misinformation raise politician support, while potentially undermining trust in media. Moreover, these false claims produce greater dividends for politicians than longstanding alternative responses to scandal, such as remaining silent or apologizing. Finally, false allegations of misinformation pay off less for videos ("deepfakes") than text stories ("fake news").

Demand for democratic accountability in policing is accelerating, yet little is understood about how law enforcement executives engage in policy learning around civilian oversight. This paper shares the results of a novel survey experiment administered to all U.S. police chiefs and sheriffs. We assess whether police executives’ attitudes towards civilian oversight are responsive to 1) state-level public opinion (drawing on an n=16,840 survey) and 2) prior adoption of civilian review boards in large agencies. Results from over 1,300 police executives reveal that law enforcement leaders are responsive to peer adoption but much less to public opinion, despite overwhelming support amongst voters. Further, we find that agencies with an established oversight board are highly supportive of their existence, while elected sheriffs are much less likely to support civilian oversight. Our results indicate that policy learning and reform around civilian oversight are possible, though sources of reform are not themselves primarily democratic.

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