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AbstractA number of findings in the field of machine learning have given rise to questions about what it means for automated scoring- or decision-making systems to be fair. One center of gravity in this discussion is whether such systems ought to satisfy classification parity (which requires parity in predictive performance across groups, defined by protected attributes) or calibration (which requires similar predictions to have similar meanings across groups, defined by protected attributes). Central to this discussion are impossibility results, which show that classification parity and calibration are often incompatible. This paper aims to argue that classification parity, calibration, and a newer, interesting measure called counterfactual fairness are unsatisfactory measures of fairness, offer a general diagnosis of the failure of these measures, and sketch an alternative approach to understanding fairness in machine learning.

1. Introduction -- 2. Respect for Humanity -- 3. Mobile Devices and Autonomy: Individual-Level Effects -- 4. The Duty to Promote Digital Minimalism in Ourselves -- 5. The Duty to Promote Digital Minimalism in Others I: Duties of Virtue -- 6. The Duty to Promote Digital Minimalism in Others II: Duties of Right -- 7. The Duty to Promote Digital Minimalism in Group Agents -- 8. Conclusion.

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work... the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using these case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one's decision to take that action to an algorithm, thereby laundering one's agency.