MQG4AI Towards Responsible High-risk AI -- Illustrated for Transparency Focusing on Explainability Techniques

TL;DR

This paper presents a flexible AI lifecycle management approach that integrates responsible AI principles, emphasizing explainability and transparency, through customizable quality gates aligned with ethical, regulatory, and technical requirements.

Contribution

It introduces a novel methodology based on Quality Gates for AI lifecycle planning, specifically focusing on explainability and transparency in model development.

Findings

A customizable framework for AI lifecycle management.

Alignment of explanation quality evaluation with responsible AI guidelines.

Enhanced transparency through continuous information linking.

Abstract

As artificial intelligence (AI) systems become increasingly integrated into critical domains, ensuring their responsible design and continuous development is imperative. Effective AI quality management (QM) requires tools and methodologies that address the complexities of the AI lifecycle. In this paper, we propose an approach for AI lifecycle planning that bridges the gap between generic guidelines and use case-specific requirements (MQG4AI). Our work aims to contribute to the development of practical tools for implementing Responsible AI (RAI) by aligning lifecycle planning with technical, ethical and regulatory demands. Central to our approach is the introduction of a flexible and customizable Methodology based on Quality Gates, whose building blocks incorporate RAI knowledge through information linking along the AI lifecycle in a continuous manner, addressing AIs evolutionary character. For our present contribution, we put a particular emphasis on the Explanation stage during model development, and illustrate how to align a guideline to evaluate the quality of explanations with MQG4AI, contributing to overall Transparency.