A Replication of “Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making”

ABSTRACT 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.

Authors

Institutions

Publication Details

Journal
Public Administration
Published
2025-08-27
DOI
https://doi.org/10.1111/padm.70015
Citations
3
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
7.10
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Replication of “Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making”

Hua-Yu Zhou, Xuemei Fang, Shufa CHEN
3 citations
Public Administration
Ethics and Social Impacts of AI
7.10
article

A Replication of “Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making”

Hua-Yu Zhou, Xuemei Fang, Shufa CHEN
article en
3 citations

Abstract

ABSTRACT 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.

Public AdministrationVol. 103(4)
East China University of Science and Technology (CN), Hunan University (CN), East China University of Technology (CN), Shanghai Ocean University (CN)
Openalex Percentile: Top 3%
Ethics and Social Impacts of AI
7.10
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Replication of “Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making” — Hua-Yu Zhou, Xuemei Fang, et al. · Public Administration (2025) | TGRS Research Map | TGRS