What is it about?
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.
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Why is it important?
Our study reveals that, on numerous occasions, even when TAI issues are identified, they are not addressed by practitioners - either directly or by third parties - due to business constraints such as limited time, financial constraints, or declining performance. Also, as a general remark, there is a pressing need for guidelines, knowledge bases, and tools that can help practitioners implement TAI principles throughout the entire SDLC.
Perspectives
There is one of the first surveys about Trustworthy AI and perspectives on how practitioners address the related challenges. This work is important because provides interesting practical insights and next steps to follow.
Domenico Gigante
Universita degli Studi di Bari Aldo Moro
Read the Original
This page is a summary of: Trustworthy AI in practice: an analysis of practitioners' needs and challenges, June 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3661167.3661214.
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