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Cited by 90

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

AI Risk Assessment: A Scenario-Based, Proportional Methodology for the AI Act

Novelli, Claudio, Casolari, Federico, Rotolo, Antonino, Taddeo, Mariarosaria, Floridi, Luciano·2024·Digital Society
DOI 10.1007/s44206-024-00095-1
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Abstract

The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA with a framework developed by the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This approach enables a nuanced analysis of AI risk by exploring the interplay between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We further refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment. Finally, we present three uses of this approach under the AIA: to implement the Regulation, to assess the significance of risks, and to develop internal risk management systems for AI deployers.

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Crossref · 10.1007/s44206-024-00095-1

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Fig. 2 Three categories of increasingly complex climate change risk by (Simpson et al., 2021)
Fig. 2 Three categories of increasingly complex climate change risk by (Simpson et al., 2021)CC-BY · From the PDF

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Citations

Cites

Research works printed but not linked: 3

  1. Garrett, B., & Monahan, J. (2020). Judging risk. California Law Review, 108(2), 439–493.
  2. Mahler, T. (2007). Defining legal risk. SSRN Scholarly Paper. Rochester, NY. https://papers.ssrn.com/abstract=1014364
  3. Sunstein, C. R. (2004). Risk and reason. Cambridge Books, Cambridge University Press. https://ideas.repec.org/b/cup/cbooks/9780521016254.html

Cited by

See all 90

Metadata

Authors
Novelli, C., Casolari, F., Rotolo, A., Taddeo, M., & Floridi, L.
ORCID
Novelli, Claudio
Venue
Digital Society
Publisher
Springer Science and Business Media LLC
Published
2024-03-07
Volume
3
Issue
1
ISSN
  • 2731-4650
  • 2731-4669
DOI
10.1007/s44206-024-00095-1
Cited by
90
References
46
Language
en
Licence
Funders
Alma Mater Studiorum - Università di Bologna

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