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Big data and algorithmic decision-making: can transparency restore accountability?

Published: 25 September 2017 Publication History

Abstract

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Can transparency contribute to restoring accountability for such systems? Several objections are examined: the loss of privacy when data sets become public, the perverse effects of disclosure of the very algorithms themselves ('gaming the system' in particular), the potential loss of competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms are inherently opaque. It is concluded that transparency is certainly useful, but only up to a point: extending it to the public at large is normally not to be advised. Moreover, in order to make algorithmic decisions understandable, models of machine learning to be used should either be interpreted ex post or be interpretable by design ex ante.

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Published In

cover image ACM SIGCAS Computers and Society
ACM SIGCAS Computers and Society  Volume 47, Issue 3
September 2017
135 pages
ISSN:0095-2737
DOI:10.1145/3144592
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2017
Published in SIGCAS Volume 47, Issue 3

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Author Tags

  1. accountability
  2. algorithm
  3. interpretability
  4. machine learning
  5. opacity
  6. transparency

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  • (2024)When the bridge is not human: Algorithmic interference in forming social relationships through the manipulation of weak tiesJournal of Consumer Affairs10.1111/joca.1258658:2(606-629)Online publication date: 23-May-2024
  • (2024)Automated Decision-Making in the Public Sector: A Multidisciplinary Literature ReviewElectronic Government10.1007/978-3-031-70274-7_15(237-253)Online publication date: 3-Sep-2024
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