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Public Administration Review
Article . 2020 . Peer-reviewed
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Public Administration Review
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Article . 2020
License: CC BY
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Public Administration Review
Article . 2021 . Peer-reviewed
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Accountable Artificial Intelligence: Holding Algorithms to Account

Authors: Madalina Busuioc;

Accountable Artificial Intelligence: Holding Algorithms to Account

Abstract

AbstractArtificial intelligence (AI) algorithms govern in subtle yet fundamental ways the way we live and are transforming our societies. The promise of efficient, low‐cost, or “neutral” solutions harnessing the potential of big data has led public bodies to adopt algorithmic systems in the provision of public services. As AI algorithms have permeated high‐stakes aspects of our public existence—from hiring and education decisions to the governmental use of enforcement powers (policing) or liberty‐restricting decisions (bail and sentencing)—this necessarily raises important accountability questions: What accountability challenges do AI algorithmic systems bring with them, and how can we safeguard accountability in algorithmic decision‐making? Drawing on a decidedly public administration perspective, and given the current challenges that have thus far become manifest in the field, we critically reflect on and map out in a conceptually guided manner the implications of these systems, and the limitations they pose, for public accountability.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
356
Top 0.1%
Top 1%
Top 1%
Green
hybrid