Accountability in AI: From Principles to Industry-specific Accreditation

TL;DR

This paper explores AI accountability by proposing an ecosystem framework, highlighting the need for transparency and industry-specific accreditation, illustrated through a gambling sector case study.

Contribution

It introduces an AI accountability ecosystem model and demonstrates the importance of industry-specific principles through a detailed gambling sector case study.

Findings

Unbalanced accountability ecosystem with transparency gaps

Need for industry-specific accountability principles

Implementation of bias mitigation and explainability in gambling AI

Abstract

Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing public interest and concern. This paper draws on literature from public policy and governance to make two contributions. First, we propose an AI accountability ecosystem as a useful lens on the system, with different stakeholders requiring and contributing to specific accountability mechanisms. We argue that the present ecosystem is unbalanced, with a need for improved transparency via AI explainability and adequate documentation and process formalisation to support internal audit, leading up eventually to external accreditation processes. Second, we use a case study in the gambling sector to illustrate in a subset of the overall ecosystem the need for industry-specific accountability principles and processes. We define and evaluate critically the implementation of key accountability principles in the gambling industry, namely addressing algorithmic bias and model explainability, before concluding and discussing directions for future work based on our findings. Keywords: Accountability, Explainable AI, Algorithmic Bias, Regulation.