Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development

• URL: http://arxiv.org/abs/2403.15481v2
• Date: Wed, 31 Jul 2024 14:47:24 GMT
• Title: Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development
• Authors: Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan,
• Abstract summary: There is a lack of empirical studies focused on understanding the perspectives and experiences of AI practitioners in developing a fair AI/ML system. 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 system, the consequences of developing an unfair AI/ML system, and the strategies they employ to ensure AI/ML system fairness. This study provides actionable insights to enhance AI/ML fairness, which may promote fairer systems, reduce bias, and foster public trust in AI technologies.
• Score: 11.846525587357489
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 perspectives and experiences of AI practitioners in developing a fair AI/ML system. Understanding AI practitioners' perspectives and experiences on the fairness of AI/ML systems are 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 systems. 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 system, the consequences of developing an unfair AI/ML system, and the strategies they employ to ensure AI/ML system fairness. We developed a framework showcasing the relationship between AI practitioners' understanding of 'fair AI/ML' system and (i) their challenges in its development, (ii) the consequences of developing an unfair AI/ML system, and (iii) strategies used to ensure AI/ML system fairness. By exploring AI practitioners' perspectives and experiences, this study provides actionable insights to enhance AI/ML fairness, which may promote fairer systems, reduce bias, and foster public trust in AI technologies. 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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