Foundations for Risk Assessment of AI in Protecting Fundamental Rights

TL;DR

This paper proposes a conceptual framework for qualitative risk assessment of AI systems, focusing on legal compliance and fundamental rights protection within the EU AI Act, using defeasible reasoning and definitional balancing.

Contribution

It introduces a layered, philosophical approach for analyzing AI risks to fundamental rights, integrating legal reasoning techniques into AI risk assessment.

Findings

Framework addresses legal compliance complexities

Incorporates defeasible reasoning for dynamic legal decision-making

Lays groundwork for formal models and algorithms for AI risk assessment

Abstract

This chapter introduces a conceptual framework for qualitative risk assessment of AI, particularly in the context of the EU AI Act. The framework addresses the complexities of legal compliance and fundamental rights protection by itegrating definitional balancing and defeasible reasoning. Definitional balancing employs proportionality analysis to resolve conflicts between competing rights, while defeasible reasoning accommodates the dynamic nature of legal decision-making. Our approach stresses the need for an analysis of AI deployment scenarios and for identifying potential legal violations and multi-layered impacts on fundamental rights. On the basis of this analysis, we provide philosophical foundations for a logical account of AI risk analysis. In particular, we consider the basic building blocks for conceptually grasping the interaction between AI deployment scenarios and fundamental rights, incorporating in defeasible reasoning definitional balancing and arguments about the contextual promotion or demotion of rights. This layered approach allows for more operative models of assessment of both high-risk AI systems and General Purpose AI (GPAI) systems, emphasizing the broader applicability of the latter. Future work aims to develop a formal model and effective algorithms to enhance AI risk assessment, bridging theoretical insights with practical applications to support responsible AI governance.