Normative Challenges of Risk Regulation of Artificial Intelligence and Automated Decision-Making
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Abstract (arXiv)
Recent proposals aiming at regulating artificial intelligence (AI) and automated decision-making (ADM) suggest a particular form of risk regulation, i.e. a risk-based approach. The most salient example is the Artificial Intelligence Act (AIA) proposed by the European Commission. The article addresses challenges for adequate risk regulation that arise primarily from the specific type of risks involved, i.e. risks to the protection of fundamental rights and fundamental societal values. They result mainly from the normative ambiguity of the fundamental rights and societal values in interpreting, specifying or operationalising them for risk assessments. This is exemplified for (1) human dignity, (2) informational self-determination, data protection and privacy, (3) justice and fairness, and (4) the common good. Normative ambiguities require normative choices, which are distributed among different actors in the proposed AIA. Particularly critical normative choices are those of selecting normative conceptions for specifying risks, aggregating and quantifying risks including the use of metrics, balancing of value conflicts, setting levels of acceptable risks, and standardisation. To avoid a lack of democratic legitimacy and legal uncertainty, scientific and political debates are suggested.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Nov 2022.
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Enhancing Marker Scoring Accuracy through Ordinal Confidence Modelling in Educational Assessmentscited by 4
- 2025Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Makingcited by 2
- 2026Informing AI Policy Assessment using Large-Scale Simulation of Interventionscited by 0
- 2026Informing AI Policy Assessment using Large-Scale Simulation of Interventions20 Apr 2026
- 2025Enhancing Marker Scoring Accuracy through Ordinal Confidence Modelling in Educational Assessments29 May 2025
- 2025Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making24 Jan 2025