Trustworthy AI in practice: an analysis of practitioners' needs and challenges
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 6 of its 48 references that are in the map. First posted on arXiv 15 May 2024.
Builds on
6 of 48 references.
References · 6
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
- 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 67
- 2023A Rapid Review of Responsible AI frameworks: How to guide the development of ethical AIcited by 5
- 2023MLOps Spanning Whole Machine Learning Life Cycle: A Surveycited by 3
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.