Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures Through AI Systems
Year: 2024
Type: article
Abstract: The prevailing discourse around AI ethics lacks the language and formalism necessary to capture the diverse ethical concerns that emerge when AI systems interact with individuals. Drawing on Sen and Nussbaum’s capability approach, we present a framework formalizing a network of ethical concepts and entitlements necessary for AI systems to confer meaningful benefit or assistance to stakeholders. Such systems enhance stakeholders’ ability to advance their life plans and well-being while upholding their fundamental rights. We characterize two necessary conditions for morally permissible interactions between AI systems and those impacted by their functioning, and two sufficient conditions for realizing the ideal of meaningful benefit. We then contrast this ideal with several salient failure modes, namely, forms of social interactions that constitute unjustified paternalism, coercion, deception, exploitation and domination. The proliferation of incidents involving AI in high-stakes domains underscores the gravity of these issues and the imperative to take an ethics-led approach to AI systems from their inception.
Source: Minds and Machines
Authors: Alex John London, Hoda Heidari
Institution: Carnegie Mellon University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
Awards: FAI: Fair AI in Public Policy - Achieving Fair Societal Outcomes in ML Applications to Education, Criminal Justice, and Health and Human Services, AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING), AI Institute for Societal Decision Making (AI-SDM)
OpenAlex ID: W4402942441