Trustworthy AI in practice: an analysis of practitioners' needs and challenges
It addresses the problem of bridging theory and practice in Trustworthy AI for practitioners, but is incremental as it builds on existing frameworks.
The study investigated AI practitioners' challenges and needs in implementing Trustworthy AI principles, finding gaps in the use of existing frameworks and providing practical recommendations to support development.
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.