Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development
arxiv(2024)
摘要
The rise in the use of AI/ML applications across industries has sparked more
discussions about the fairness of AI/ML in recent times. While prior research
on the fairness of AI/ML exists, there is a lack of empirical studies focused
on understanding the views and experiences of AI practitioners in developing a
fair AI/ML. Understanding AI practitioners' views and experiences on the
fairness of AI/ML is important because they are directly involved in its
development and deployment and their insights can offer valuable real-world
perspectives on the challenges associated with ensuring fairness in AI/ML. We
conducted semi-structured interviews with 22 AI practitioners to investigate
their understanding of what a 'fair AI/ML' is, the challenges they face in
developing a fair AI/ML, the consequences of developing an unfair AI/ML, and
the strategies they employ to ensure AI/ML fairness. We developed a framework
showcasing the relationship between AI practitioners' understanding of 'fair
AI/ML' and (i) their challenges in its development, (ii) the consequences of
developing an unfair AI/ML, and (iii) strategies used to ensure AI/ML fairness.
Additionally, we also identify areas for further investigation and offer
recommendations to aid AI practitioners and AI companies in navigating
fairness.
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