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While prior research on the fairness of AI\/ML exists, there is a lack of empirical studies focused on understanding perspectives and experiences of AI practitioners in developing a fair AI\/ML system. Understanding AI practitioners\u2019 perspectives and experiences on the fairness of AI\/ML systems 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 systems. We conducted semi-structured interviews with 22 AI practitioners to investigate their <jats:italic>understanding<\/jats:italic> of what a \u2018fair AI\/ML\u2019 is, the <jats:italic>challenges<\/jats:italic> they face in developing a fair AI\/ML system, the <jats:italic>consequences<\/jats:italic> of developing an unfair AI\/ML system, and the <jats:italic>strategies<\/jats:italic> they employ to ensure AI\/ML system fairness. By exploring AI practitioners\u2019 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.<\/jats:p>","DOI":"10.1007\/s10664-025-10650-0","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T04:19:46Z","timestamp":1744863586000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Navigating fairness: practitioners\u2019 understanding, challenges, and strategies in AI\/ML development"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6183-0492","authenticated-orcid":false,"given":"Aastha","family":"Pant","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashina","family":"Hoda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chakkrit","family":"Tantithamthavorn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Burak","family":"Turhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"10650_CR1","unstructured":"Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias. 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