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Administration by algorithm: A risk management framework

Administration by algorithm: A risk management framework Algorithmic decision-making is neither a recent phenomenon nor one necessarily associated with artificial intelligence (AI), though advances in AI are increasingly resulting in what were heretofore human decisions being taken over by, or becoming dependent on, algorithms and technologies like machine learning. Such developments promise many potential benefits, but are not without certain risks. These risks are not always well understood. It is not just a question of machines making mistakes; it is the embedding of values, biases and prejudices in software which can discriminate against both individuals and groups in society. Such biases are often hard either to detect or prove, particularly where there are problems with transparency and accountability and where such systems are outsourced to the private sector. Consequently, being able to detect and categorise these risks is essential in order to develop a systematic and calibrated response. This paper proposes a simple taxonomy of decision-making algorithms in the public sector and uses this to build a risk management framework with a number of components including an accountability structure and regulatory governance. This framework is designed to assist scholars and practitioners interested in ensuring structured accountability and legal regulation of AI in the public sphere. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Information Polity IOS Press

Administration by algorithm: A risk management framework

Bannister, Frank; Connolly, Regina
Information Polity , Volume 25 (4): 20 – Dec 4, 2020
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Publisher
IOS Press
Copyright
Copyright © 2020 © 2020 – IOS Press and the authors. All rights reserved
ISSN
1570-1255
eISSN
1875-8754
DOI
10.3233/IP-200249
Publisher site
See Article on Publisher Site

Abstract

Algorithmic decision-making is neither a recent phenomenon nor one necessarily associated with artificial intelligence (AI), though advances in AI are increasingly resulting in what were heretofore human decisions being taken over by, or becoming dependent on, algorithms and technologies like machine learning. Such developments promise many potential benefits, but are not without certain risks. These risks are not always well understood. It is not just a question of machines making mistakes; it is the embedding of values, biases and prejudices in software which can discriminate against both individuals and groups in society. Such biases are often hard either to detect or prove, particularly where there are problems with transparency and accountability and where such systems are outsourced to the private sector. Consequently, being able to detect and categorise these risks is essential in order to develop a systematic and calibrated response. This paper proposes a simple taxonomy of decision-making algorithms in the public sector and uses this to build a risk management framework with a number of components including an accountability structure and regulatory governance. This framework is designed to assist scholars and practitioners interested in ensuring structured accountability and legal regulation of AI in the public sphere.

Journal

Information Polity – IOS Press

Published: Dec 4, 2020

References

  • Automated marking system for short answer examination (AMS-SAE)
    Ab Aziz
  • Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability
    Ananny
  • Illusions of Accountability: Credit and Blame Sensemaking in Public Administration
    Anderson