A Replication of “Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making”
With the advancement of artificial intelligence, algorithms are transforming the operations of the public sector. However, lack of algorithm transparency may result in issues such as algorithmic bias and accountability challenges, ultimately undermining public trust. Based on the principles of replication experiments and procedural justice theory, this study conducted a replication of Grimmelikhuijsen in a Chinese context. The replication reaffirmed Grimmelikhuijsen's core findings that algorithmic explainability enhances public trust, thus demonstrating its potential to foster trust across cultural contexts. Unlike the original research, the results indicated that accessibility remains important for fostering trust. The impact of transparency varies across decision contexts, with greater effects in high‐discretion situations. By replicating Grimmelikhuijsen, the current research not only provides new empirical support for procedural justice theory, but it also offers practical insights into configuring algorithmic transparency within a public administration context.
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Mark Bovens, Stavros Zouridis
Richard W. Brislin
Jenna Burrell
Madalina Busuioc
Jacob Cohen
Maria Cucciniello, Gregory A. Porumbescu, Stephan Grimmelikhuijsen
Yi Deng, Yu Sun
Berkeley J. Dietvorst, Joseph P. Simmons, Cade Massey
Franz Faul, Edgar Erdfelder, Axel Buchner et al.
Sarah Giest, Stephan Grimmelikhuijsen
Ella Glikson, Anita Williams Woolley
Stephan Grimmelikhuijsen, Sebastian Jilke, Asmus Leth Olsen et al.
Stephan Grimmelikhuijsen, Gregory Porumbescu, Boram Hong et al.
Geert Hofstede
Daniel S. Schiff, Kaylyn Jackson Schiff, Patrick Pierson
Friso Selten, Marcel Robeer, Stephan Grimmelikhuijsen
Eric W. K. Tsang, Kai-Man Kwan
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- Published
- Aug 27, 2025
- Vol/Issue
- 103(4)
- Pages
- 1232-1252
- License
- View
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