Justified Evidence Collection for Argument-based AI Fairness Assurance

TL;DR

This paper presents a systems-engineering framework supported by software tools for dynamic, argument-based assurance of AI fairness, enabling continuous evidence collection and stakeholder engagement throughout the AI system lifecycle.

Contribution

It introduces a novel two-stage framework for operationalising dynamic argument-based assurance of AI fairness, integrating multi-stakeholder governance and continuous evidence gathering.

Findings

Framework demonstrated effective in a finance case study

Supports continuous monitoring of fairness evidence

Enhances transparency and accountability in AI fairness assurance

Abstract

It is well recognised that ensuring fair AI systems is a complex sociotechnical challenge, which requires careful deliberation and continuous oversight across all stages of a system's lifecycle, from defining requirements to model deployment and deprovisioning. Dynamic argument-based assurance cases, which present structured arguments supported by evidence, have emerged as a systematic approach to evaluating and mitigating safety risks and hazards in AI-enabled system development and have also been extended to deal with broader normative goals such as fairness and explainability. This paper introduces a systems-engineering-driven framework, supported by software tooling, to operationalise a dynamic approach to argument-based assurance in two stages. In the first stage, during the requirements planning phase, a multi-disciplinary and multi-stakeholder team define goals and claims to be established (and evidenced) by conducting a comprehensive fairness governance process. In the second stage, a continuous monitoring interface gathers evidence from existing artefacts (e.g. metrics from automated tests), such as model, data, and use case documentation, to support these arguments dynamically. The framework's effectiveness is demonstrated through an illustrative case study in finance, with a focus on supporting fairness-related arguments.