Trustworthy AI in practice: an analysis of practitioners' needs and challenges

TL;DR

This paper investigates AI practitioners' perspectives, challenges, and needs regarding Trustworthy AI principles through surveys and interviews, providing insights and recommendations to support responsible AI development.

Contribution

It offers empirical insights into practitioners' real-world challenges and needs in implementing Trustworthy AI, which are underexplored in existing frameworks.

Findings

Practitioners face specific challenges in applying TAI principles.

There is a gap between existing frameworks and practical needs.

Recommendations are provided to bridge this gap.

Abstract

Recently, there has been growing attention on behalf of both academic and practice communities towards the ability of Artificial Intelligence (AI) systems to operate responsibly and ethically. As a result, a plethora of frameworks and guidelines have appeared to support practitioners in implementing Trustworthy AI applications (TAI). However, little research has been done to investigate whether such frameworks are being used and how. In this work, we study the vision AI practitioners have on TAI principles, how they address them, and what they would like to have - in terms of tools, knowledge, or guidelines - when they attempt to incorporate such principles into the systems they develop. Through a survey and semi-structured interviews, we systematically investigated practitioners' challenges and needs in developing TAI systems. Based on these practical findings, we highlight recommendations to help AI practitioners develop Trustworthy AI applications.