Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance

TL;DR

This paper proposes a stakeholder-first approach to help technologists design AI systems that are transparent and compliant with regulations, addressing a gap in current governance frameworks.

Contribution

It introduces a novel stakeholder-centric framework for AI transparency and provides a practical case study demonstrating its application.

Findings

Stakeholder-first approach effectively guides transparent AI design.

The framework facilitates regulatory compliance in real-world scenarios.

Case study validates practical utility of the approach.

Abstract

Increasingly, laws are being proposed and passed by governments around the world to regulate Artificial Intelligence (AI) systems implemented into the public and private sectors. Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them. Yet, almost all AI governance documents to date have a significant drawback: they have focused on what to do (or what not to do) with respect to making AI systems transparent, but have left the brunt of the work to technologists to figure out how to build transparent systems. We fill this gap by proposing a novel stakeholder-first approach that assists technologists in designing transparent, regulatory compliant systems. We also describe a real-world case-study that illustrates how this approach can be used in practice.