What we owe to decision-subjects: beyond transparency and explanation in automated decision-making

Philosophical Studies 2003:1-31 (2023)
  Copy   BIBTEX


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.

Similar books and articles

AI, Opacity, and Personal Autonomy.Bram Vaassen - 2022 - Philosophy and Technology 35 (4):1-20.


Added to PP

527 (#35,487)

6 months
365 (#5,359)

Historical graph of downloads
How can I increase my downloads?

Author Profiles

Jeff Behrends
Harvard University
John Basl
Northeastern University
David Gray Grant
University of Florida

Citations of this work

No citations found.

Add more citations