FairnessTransparencyAccountabilitycs.SE
Responsible AI in the Software Industry: A Practitioner-Centered Perspective
Matheus de Morais Leça, Mariana Bento, Ronnie de Souza Santos
December 10, 2024
2025 IEEE/ACM International Workshop on Responsible AI Engineering (RAIE)
9 citations
1 influential
Key Takeaways
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Fairness, inclusiveness, and reliability dominate current practice: Software practitioners actively work on these three Responsible AI principles, suggesting they have clearer implementation pathways than other ethical guidelines.
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Transparency and accountability are significantly neglected: The study found that transparency and accountability receive "comparatively less attention" in practitioners' work, indicating a critical gap between stated Responsible AI principles and what companies actually implement.
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Responsible AI implementation lacks practical frameworks: The research reveals that while ethical principles exist, practitioners struggle with operationalizing them in real software development work, pointing to a need for more concrete, actionable guidance beyond high-level principles.
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Practitioner-centered insights are essential for progress: By interviewing 25 software practitioners directly, the study demonstrates that understanding real-world challenges and methods is crucial for developing Responsible AI strategies that actually work in industry settings, rather than relying solely on theoretical frameworks.
Abstract
Responsible AI principles provide ethical guidelines for developing AI systems, yet their practical implementation in software engineering lacks thorough investigation. Therefore, this study explores the practices and challenges faced by software practitioners in aligning with these principles. Through semi-structured interviews with 25 practitioners, we investigated their methods, concerns, and strategies for addressing Responsible AI in software development. Our findings reveal that while practitioners frequently address fairness, inclusiveness, and reliability, principles such as transparency and accountability receive comparatively less attention in their practices. This scenario highlights gaps in current strategies and the need for more comprehensive frameworks to fully operationalize Responsible AI principles in software engineering.