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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The notion of using data to predict future outcomes is far from new. Even highly technical products that performed "predictive analytics" analysis have already been available to enterprise organizations for many years. The notion of developing and deploying custom-built predictive solutions, however, have, for the most part, been the exclusive domain of Fortune 500 companies. The rarity of predictive analytics in the enterprise is mostly due to the technical complexity needed to create, train, and deploy the complex AI and Machine Learning (ML) models required to successfully develop predictive solutions. Over the past few years, the world of AI and ML development has seen rapid change.

To gain broad insight into the AI and ML landscape beyond the market hype. To gain high level understanding of the architectures, algorithms, platforms, APIs, and libraries for AI/ML development, current and future AI/ML application domains, and domain-specific models and algorithms. To gain pragmatic understanding of career prospects and emerging opportunities in AI/ML and how to prepare for them. To gain broad insight into the AI and ML landscape beyond the market hype. To gain high level understanding of the architectures, algorithms, platforms, APIs, and libraries for AI/ML development, current and future AI/ML application domains, and domain-specific models and algorithms.