Normative Challenges of Risk Regulation of AI and ADM
The paper addresses the intricate normative challenges associated with regulating AI and automated decision-making (ADM) systems, specifically through a risk-based regulatory approach. The discussion is positioned within the framework of current legislative efforts, such as the European Commission's Artificial Intelligence Act (AIA).
Risk-Based Versus Rights-Based Approaches
The paper highlights the choice between a risk-based and a rights-based regulatory approach. A risk-based approach prioritizes regulatory activities based on perceived risk levels, which can be seen as resource-efficient but might fail to fully protect fundamental rights. In contrast, a rights-based approach enforces regulations consistently across contexts to ensure comprehensive rights protection, which may incur higher administrative costs.
Ambiguities in Fundamental Rights and Societal Values
A critical issue in AI and ADM regulation is the normative ambiguity related to fundamental rights like human dignity, justice, and the common good. These ambiguities demand careful normative choices concerning their interpretation and operationalization for risk assessments.
Examples include:
- Human Dignity: The paper questions how human dignity can be quantified or protected in automated systems that rely on data-driven generalizations, which might reduce individual recognition.
- Informational Self-Determination and Privacy: There's a need for new criteria adapted to AI capabilities, such as re-identification risks from non-personal data.
- Justice and Fairness: Fairness metrics may overlook individualized justice, as they often focus on group fairness over individual rights.
- Common Good: This concept, though emphasized in policy debates, lacks concrete principles for practical regulatory frameworks.
Importance of Operationalizing Risks
Effective risk regulation requires clear criteria to identify and manage risks to societal values and rights. However, ethical guidelines and existing legislation often lack the specificity needed for such operationalization, demanding further normative decisions to fill these gaps.
Implementation Challenges
The AIA proposes a hybrid governance approach, involving self-certification by providers, and oversight by standardization bodies. This could lead to democratic legitimacy concerns as these bodies might lack the required transparency and accountability.
Implications for Regulation
The dispersion of normative decision-making to various actors without explicit guidelines may lead to inconsistencies and potential arbitrariness in protecting fundamental rights. This necessitates a broader discourse on normative choices related to AI risks involving public participation and transparency.
Conclusion
The paper suggests that for AI and ADM regulation to be legitimate and effective, challenges related to normative ambiguities and the delegation of regulatory responsibilities need to be addressed. The emphasis is placed on ensuring that these choices reflect democratic processes and are informed by comprehensive societal discourse. This involves refining the balance between protecting fundamental rights and fostering innovation within AI systems. Future regulatory frameworks should thus evolve through ongoing scientific and public engagement, rather than remaining static.