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<p>Responsible AI (RAI) entails developing, using, and governing AI in a human-centred way to ensure its trustworthiness and alignment with human values. Organizations attempt to enact RAI through guiding principles that aim to minimize threats such as bias and privacy violations and enhance outcomes such as transparency and fairness. In the empirical study we report in this paper, we share insights about how AI experts view RAI principles, including relationships among them, these principles, and the enactment of RAI principles. Employing a sequential mixed-methods approach, we analyse survey data from 135 AI experts from Europe and America using fuzzy-set Qualitative Comparative Analysis (fsQCA), complemented by follow-up interviews. Our analysis identifies five equifinal combinations of RAI principles associated with high overall RAI enactment. The findings reveal the crucial role of accountability and safety-reliability principles and highlight the contextual variability of other principles. This research contributes a nuanced understanding of RAI enactment, moving beyond individual principles to demonstrate their complex interplay and their contextual dependencies in achieving responsible AI practices.</p>

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Enacting Responsible AI: A Configurational Analysis of AI Principles in Practice

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月23日

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  • Pouria Akbarighatar,
  • Ilias O. Pappas,
  • Sandeep Purao,
  • Polyxeni Vassilakopoulou

摘要

Responsible AI (RAI) entails developing, using, and governing AI in a human-centred way to ensure its trustworthiness and alignment with human values. Organizations attempt to enact RAI through guiding principles that aim to minimize threats such as bias and privacy violations and enhance outcomes such as transparency and fairness. In the empirical study we report in this paper, we share insights about how AI experts view RAI principles, including relationships among them, these principles, and the enactment of RAI principles. Employing a sequential mixed-methods approach, we analyse survey data from 135 AI experts from Europe and America using fuzzy-set Qualitative Comparative Analysis (fsQCA), complemented by follow-up interviews. Our analysis identifies five equifinal combinations of RAI principles associated with high overall RAI enactment. The findings reveal the crucial role of accountability and safety-reliability principles and highlight the contextual variability of other principles. This research contributes a nuanced understanding of RAI enactment, moving beyond individual principles to demonstrate their complex interplay and their contextual dependencies in achieving responsible AI practices.