What we owe to decision-subjects

Beyond Transparency and Explanation in Automated Decision-Making

Bibliographic Data

ID7164580
AuthorsDavid Gray Grant (0000-0002-2000-6907, University of Florida), Jeff Behrends (Harvard University Press), John Basl (0000-0003-2056-4201, Northeastern University, corresponding author)
Year2025
Volume182
Issue1
Pages55-85
Publication date2025-01-01
Open AccessYes
TypeARTICLE
VenuePhilosophical Studies (JOURNAL)
Journal identifiersISSN: 0031-8116 • E-ISSN: 1573-0883
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11098-023-02013-6
OpenAlexW4385953451
LanguageEN
Citations received12
References cited63

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making

Business decision mapping · Computer security · Decision support system · Economics · Management science · Process (computing) · Transparency (behavior) · Adversarial Robustness in Machine Learning · Artificial Intelligence · Computer Science · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI

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