Enhanced well-being assessment as basis for the practical implementation of ethical and rights-based normative principles for AI

TL;DR

This paper proposes an enhanced well-being impact assessment framework for AI systems, aiming to facilitate the practical implementation of ethical and rights-based principles through a human-centered, iterative, and cooperative approach.

Contribution

It introduces a novel impact assessment framework for AI that incorporates well-being metrics and a new testing infrastructure for stakeholder collaboration.

Findings

Framework enables iterative impact assessment and inclusion of new impacts.

Supports human-centered, algorithmically-assisted understanding of AI impacts.

Proposes infrastructure for stakeholder cooperation in AI impact evaluation.

Abstract

Artificial Intelligence (AI) has an increasing impact on all areas of people's livelihoods. A detailed look at existing interdisciplinary and transdisciplinary metrics frameworks could bring new insights and enable practitioners to navigate the challenge of understanding and assessing the impact of Autonomous and Intelligent Systems (A/IS). There has been emerging consensus on fundamental ethical and rights-based AI principles proposed by scholars, governments, civil rights organizations, and technology companies. In order to move from principles to real-world implementation, we adopt a lens motivated by regulatory impact assessments and the well-being movement in public policy. Similar to public policy interventions, outcomes of AI systems implementation may have far-reaching complex impacts. In public policy, indicators are only part of a broader toolbox, as metrics inherently lead to gaming and dissolution of incentives and objectives. Similarly, in the case of A/IS, there's a need for a larger toolbox that allows for the iterative assessment of identified impacts, inclusion of new impacts in the analysis, and identification of emerging trade-offs. In this paper, we propose the practical application of an enhanced well-being impact assessment framework for A/IS that could be employed to address ethical and rights-based normative principles in AI. This process could enable a human-centered algorithmically-supported approach to the understanding of the impacts of AI systems. Finally, we propose a new testing infrastructure which would allow for governments, civil rights organizations, and others, to engage in cooperating with A/IS developers towards implementation of enhanced well-being impact assessments.