Kate Vredenburgh

Algorithmic decision-making has the potential to radically reshape policy-making and policy implementation. Many of the moral examinations of AI in government take AI to be a neutral epistemic tool or the value-driven analogue of a policymaker. In this paper, I argue that AI systems in public administration are often better analogised to a street-level bureaucrat. Doing so opens up a host of questions about the moral dispositions of such AI systems. I argue that AI systems in public administration often act as indifferent bureaucrats, and that this can introduce a problematic homogeneity in the moral dispositions in administrative agencies.

This paper defends the role of lotteries in fair decision-making. It does so by targeting the use of decision thresholds to convert algorithmic predictions and classifications into decisions. Using an account of fairness from John Broome, the paper argues that decision thresholds are sometimes unfair, and that lotteries would be a fairer allocation method. It closes by dealing with two objections. First, it deals with the objection that lotteries should only be used to break ties in cases where individuals’ claims are equally strong. Here, the paper gives a new argument for Broome’s view, targeting decision criteria that are arbitrary and highly standardized. It then defends the arguments of the paper against the objection that lotteries are not morally superior to other methods of arbitrary choosing.

This article tackles the objection that revealed preferences cannot causally explain. I mount a causal explanatory defense by drawing out three conditions under which such preferences can explain well, using an example of a successful explanation employing behavioral preferences. When behavioral preferences are multiple realizable, they can causally explain behavior well. Behavioral preferences also explain when agential preferences cannot be analytically separated from the environment that produces the relevant behavior (Condition 2) and when the environment is a significant causal factor (Condition 3). Thus, there are not causal explanatory grounds to completely bar revealed preference explanations from social science.

This paper explores a neglected normative dimension of algorithmic opacity in the workplace and the labor market. It argues that explanations of algorithms and algorithmic decisions are of noninstrumental value. That is because explanations of the structure and function of parts of the social world form the basis for reflective clarification of our practical orientation toward the institutions that play a central role in our life. Using this account of the noninstrumental value of explanations, the paper diagnoses distinctive normative defects in the workplace and economic institutions which a reliance on AI can encourage, and which lead to alienation.

Chiara Cordelli’s book The Privatized State makes an important contribution to debates over the morality of public administration and widespread privatization. Cordelli argues that widespread privatization is a problem of legitimacy, as private actors impose their will unilaterally on others. Bureaucratic decision-making, by contrast, can be legitimate, within the correct institutional context and in accordance with a bureaucratic ethos. In this review, I argue that bureaucratic policymaking faces similar changes from the value of legitimacy that Cordelli raises against widespread privatization. First, I argue that for a polity subject to bureaucratic policymaking to be self-ruling, bureaucracies must incorporate more democracy; but, so doing goes against the rationale of their institutional form. Second, I argue that bureaucrats and private actors acting on behalf of the state do not have starkly different levels of free purposiveness, and that it is morally desirable for bureaucrats to have more free purposiveness than Cordelli allows, and private actors less.

This paper defends the role of lotteries in fair decision-making. It does so by targeting the use of decision thresholds to convert algorithmic predictions and classifications into decisions. Using an account of fairness from John Broome, the paper argues that decision thresholds are sometimes unfair, and that lotteries would be a fairer allocation method. It closes by dealing with two objections. First, it deals with the objection that lotteries should only be used to break ties in cases where individuals’ claims are equally strong. Here, the paper gives a new argument for Broome’s view, targeting decision criteria that are arbitrary and highly standardized. It then defends the arguments of the paper against the objection that lotteries are not morally superior to other methods of arbitrary choosing.

ABSTRACT Algorithmic decision-making has the potential to radically reshape policy-making and policy implementation. Many of the moral examinations of AI in government take AI to be a neutral epistemic tool or the value-driven analogue of a policymaker. In this paper, I argue that AI systems in public administration are often better analogised to a street-level bureaucrat. Doing so opens up a host of questions about the moral dispositions of such AI systems. I argue that AI systems in public administration often act as indifferent bureaucrats, and that this can introduce a problematic homogeneity in the moral dispositions in administrative agencies.

The particular design of any technology may have profound social implications. Computing technologies are deeply intermeshed with the activities of daily life, playing an ever more central role in how we work, learn, communicate, socialize, and participate in government. Despite the many ways they have improved life, they cannot be regarded as unambiguously beneficial or even value-neutral. Recent experience shows they can lead to unintended but harmful consequences. Some technologies are thought to threaten democracy through the spread of propaganda on online social networks, or to threaten privacy through the aggregation of datasets that include increasingly personal information, or to threaten justice when machine learning is used in such high-stakes, decision-making contexts as loan application reviews, employment procedures, or parole hearings. It is insufficient to ethically assess technology after it has produced negative social impacts, as has happened, for example, with facial recognition software that discriminates against people of color and with self-driving cars that are unable to cope with pedestrians who jay-walk. Developers of new technologies should aim to identify potential harmful consequences early in the design process and take steps to eliminate or mitigate them. This task is not easy. Designers will often have to negotiate among competing values—for instance, between efficiency and accessibility for a diverse user population, or between maximizing benefits and avoiding harm. There is no simple recipe for identifying and solving ethical problems. Computer science education can help meet these challenges by making ethical reasoning about computing technologies a central element in the curriculum. Students can learn to think not only about what technology they could create, but also whether they should create that technology. Learning to reason this way requires courses unlike those currently standard in computer science curricula. A range of university courses on topics in areas of computing, ethics, society and public policy are emerging to meet this need. Some cover computer science broadly, while others focus on specific problems like privacy and security; typically, these classes exist as stand-alone courses in the computer science curriculum. Others have integrated ethics into the teaching of introductory courses on programming, artificial intelligence, and human-computer interaction.

Chiara Cordelli’s book The Privatized State makes an important contribution to debates over the morality of public administration and widespread privatization. Cordelli argues that widespread privatization is a problem of legitimacy, as private actors impose their will unilaterally on others. Bureaucratic decision-making, by contrast, can be legitimate, within the correct institutional context and in accordance with a bureaucratic ethos. In this review, I argue that bureaucratic policymaking faces similar changes from the value of legitimacy that Cordelli raises against widespread privatization. First, I argue that for a polity subject to bureaucratic policymaking to be self-ruling, bureaucracies must incorporate more democracy; but, so doing goes against the rationale of their institutional form. Second, I argue that bureaucrats and private actors acting on behalf of the state do not have starkly different levels of free purposiveness, and that it is morally desirable for bureaucrats to have more free purposiveness than Cordelli allows, and private actors less.

Revealed preference approaches to modelling agents’ choices face two seemingly devastating explanatory objections. The no self-explanation objection imputes a problematic explanatory circularity to revealed preference approaches, while the causal explanation objection argues that, all things equal, a scientific theory should provide causal explanations, but revealed preference approaches decidedly do not. Both objections assume a view of explanation, the constraint-based view, that the revealed preference theorist ought to reject. Instead, the revealed preference theorist should adopt a unificationist account of explanation, allowing her to escape the two explanatory problems discussed in this paper.

This paper explores a neglected normative dimension of algorithmic opacity in the workplace and the labor market. It argues that explanations of algorithms and algorithmic decisions are of noninstrumental value. That is because explanations of the structure and function of parts of the social world form the basis for reflective clarification of our practical orientation toward the institutions that play a central role in our life. Using this account of the noninstrumental value of explanations, the paper diagnoses distinctive normative defects in the workplace and economic institutions which a reliance on AI can encourage, and which lead to alienation.