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Cited by 83Ethics and Social Impacts of AIDigitalization, Law, and RegulationE-Government and Public Services

Administration by algorithm: A risk management framework

Open access
Bannister, Frank, Connolly, Regina·2020·Information Polity
DOI 10.3233/ip-200249
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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.

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  • An algorithm that affects somebody in a minor way, such as adjustment of the amount of a one-time benefit, may be subject to lighter arrangements than algorithms that support life-altering decisions, such as sentencing.

    “For instance, an algorithm that affects somebody in a minor way, such as adjustment of the amount of a one-time benefit, may be subject to lighter arrangements than algorithms that support life-altering decisions, such as sentencing (Bannister and Connolly 2020).”

    From Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response (2022) · cites this paper as Bannister and Connolly 2020

    The risk scale in §4, “Governing algorithms” (page unavailable), classifies a minor change in the amount of a benefit as a minor impact and sentencing systems as life-altering. Section 4.1 assesses algorithmic risk by its consequences, but the passages found do not explicitly say that the lower-impact example should receive lighter governance arrangements; “one-time” is also not specified. (LLM, conf=0.82)

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Research works printed but not linked: 10

  1. Bostrom, N. (2014). Superintelligence. Oxford UK: Oxford University Press.
  2. Creemers, R. (2018). China's Social Credit System: An Evolving Practice of Control. Available at SSRN https://papers.ssrn.com/ sol3/papers.cfm?abstract_id=3175792.
  3. Cunningham, S. M. (1967). The Major Dimensions of Perceived risk in Cox. D.F. (Ed.) Risk Taking and Information Handling in Consumer Behaviour, Boston, Harvard University Press.
  4. Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor New York: St. Martin's Press.
  5. Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018). Fair, transparent, and accountable algorithmic decision-making processes. Philosophy & Technology, 31(4), 611-627.
  6. Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
  7. Ruppert, E. (2013). "Not Just Another Database: The Transactions that Enact Young Offenders." Computational Culture, issue 3. Accessed February 11, 2014. Available at: http://computationalculture.net/article/not-just-another-database-the-transactions- that-enact-young-offenders.
  8. Saaty, T. (2000). Fundamentals of decision making and priority theory with the analytic hierarchy process (Vol. 6). RWS Publications.
  9. Shermis, M. D., & Burstein, J. (Eds.). (2013). Handbook of automated essay evaluation: Current applications and new directions. Routledge.
  10. Veale, M., Van Kleek, M., & Binns, R. (2018, April). Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 chi conference on human factors in computing systems (p. 440). ACM.

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Authors
Bannister, F., & Connolly, R.
Affiliations
  • Trinity College Dublin (IE)
  • Dublin City University (IE)
Corresponding author
Bannister, Frank
ORCID
Venue
Information Polity
Publisher
SAGE Publications
Published
2020-12-04
Volume
25
Issue
4
Pages
471-490
ISSN
  • 1570-1255 (ISSN-L)
  • 1875-8754
DOI
10.3233/ip-200249
OpenAlex
W3112426303
Cited by
83
References
21
Citations by year
2026: 25 · 2025: 13 · 2024: 16 · 2023: 11 · 2022: 13 · 2021: 4 · 2020: 1
FWCI
9.88
Type
journal article
Language
en
Open access
Locations
  • Information Polity (publishedVersion)
  • Dublin City University Open Access Institutional Repository (Dublin City University) (submittedVersion)
  • Arrow@dit (Dublin Institute of Technology) (submittedVersion, cc-by-nc-sa)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Political Science and International Relations › E-Government and Public Services
Keywords
algorithmic decision-making, risk management framework, algorithmic bias, algorithmic discrimination, accountability, regulatory governance, AI regulation, transparency

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