  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.
