Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns
By Heike Felzmann, Eduard Fosch Villaronga, C. Lutz, Aurelia Tamó-Larrieux. Big Data & Society, 2019
367 citations recorded by the source corpus.
Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking.
Subfields: Computer Science Applications, Safety Research, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/35daaf19a893c1450dc2774fffef0c968dfc1616
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In Computer Science Applications
In Safety Research
- Timo Speith
- Will Sutherland
- Mohammad Hossein Jarrahi
- Markus Langer
- Louisa H.Y. Tsi
- Cecilia Ka Yuk Chan
In Sociology and Political Science
- Will Sutherland
- Timo Speith
- Mohammad Hossein Jarrahi
- Markus Langer
- Louisa H.Y. Tsi
- Cecilia Ka Yuk Chan
Citations in the corpus
- Trust in AI: progress, challenges, and future directions (2024, neutral): “Thus, the level of transparency reported to different stakeholders might be different (Felzmann et al., 2019; Varshney, 2019).”
- Algorithmic management in a work context (2021, background): “Recently, regulatory effort has attempted to make algorithms more transparent and explainable (Felzmann et al., 2019).”
- What Do We Want From Explainable Artificial Intelligence (XAI)? - A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research (2021, background): “Felzmann and colleagues [15] argue that different stakeholders might hold different expectations regarding the extent to which a single desideratum has to be satisfied.”