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<p>Continuing the digital revolution, AI is capable to transform our world. Thanks to its novelty, we can define how we, as a society, envision this fascinating technology to integrate with existing processes. The EU AI Act follows a risk-based approach, and we argue that addressing the human influence, which poses risks along the AI lifecycle is crucial to ensure the desired quality of the model’s transition from research to reality. Therefore, we propose a holistic approach that aims to continuously guide the involved stakeholders’ mindset, namely developers and domain experts, among others towards Responsible AI (RAI) lifecycle management. Focusing on the development view with regard to regulation, our proposed four pillars comprise the well-known concepts of <i>Generalizability</i>, <i>Adaptability</i> and <i>Translationality</i>. In addition, we introduce <i>Transversality</i> (Welsch in Vernunft: Die Zeitgenössische Vernunftkritik Und Das Konzept der Transversalen Vernunft, Suhrkamp, Frankfurt am Main, 1995), aiming to capture the multifaceted concept of <i>bias</i>, and base the four pillars on <i>Education</i>, and <i>Research</i>. Overall, we aim to provide an application-oriented summary of RAI. Our goal is to distill RAI-related principles into a concise set of concepts that emphasize implementation quality. Concluding, we introduce the ethical foundation’s transition to an applicable ethos for RAI projects as part of on-going research.</p>

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Responsible AI, ethics, and the AI lifecycle: how to consider the human influence?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月18日

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  • Miriam Elia,
  • Paula Ziethmann,
  • Julia Krumme,
  • Kerstin Schlögl-Flierl,
  • Bernhard Bauer

摘要

Continuing the digital revolution, AI is capable to transform our world. Thanks to its novelty, we can define how we, as a society, envision this fascinating technology to integrate with existing processes. The EU AI Act follows a risk-based approach, and we argue that addressing the human influence, which poses risks along the AI lifecycle is crucial to ensure the desired quality of the model’s transition from research to reality. Therefore, we propose a holistic approach that aims to continuously guide the involved stakeholders’ mindset, namely developers and domain experts, among others towards Responsible AI (RAI) lifecycle management. Focusing on the development view with regard to regulation, our proposed four pillars comprise the well-known concepts of Generalizability, Adaptability and Translationality. In addition, we introduce Transversality (Welsch in Vernunft: Die Zeitgenössische Vernunftkritik Und Das Konzept der Transversalen Vernunft, Suhrkamp, Frankfurt am Main, 1995), aiming to capture the multifaceted concept of bias, and base the four pillars on Education, and Research. Overall, we aim to provide an application-oriented summary of RAI. Our goal is to distill RAI-related principles into a concise set of concepts that emphasize implementation quality. Concluding, we introduce the ethical foundation’s transition to an applicable ethos for RAI projects as part of on-going research.