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

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.

References in corpus (4)

• A Unified Approach to Interpreting Model Predictions
• Artificial Intelligence: the global landscape of ethics guidelines
• Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
• A Rapid Review of Responsible AI frameworks: How to guide the development of ethical AI

Cited by in corpus (1)

• Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations