Public Versus Authority
How Different Targets of Algorithm‐Assisted Decision Making Impact on Citizens' Trust
Bibliographic Data
| ID | 19477709 |
|---|---|
| Authors | Yanting Ye (0009-0004-7934-8356, School of International and Public Affairs Shanghai Jiao Tong University Shanghai China), Xin Liang (0000-0002-0630-1600, School of International and Public Affairs Shanghai Jiao Tong University Shanghai China) |
| Year | 2026 |
| Volume | 26 |
| Issue | 2 |
| Publication date | 2026-05-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Public Affairs (JOURNAL) |
| Journal identifiers | ISSN: 1472-3891 • E-ISSN: 1479-1854 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/pa.70139 |
| OpenAlex | W7162137657 |
| Language | EN |
| References cited | 81 |
Amid the rapid evolution of digital governance, algorithm‐assisted decision making (ADM) has transcended its functional role in public affairs management to become a significant carrier of administrative power. Based on principal‐agent theory, we propose a “public‐algorithms‐authority” triadic governance framework. This study examines in high‐stakes governance domains, how the algorithmic targets (i.e., the public via Internet opinion regulation versus supervising authority targets through dynamic judicial review) influence citizens' trust in ADM. We conducted a survey experiment in Shanghai, recruiting 1207 participants, to compare two empirical cases: ADM for “Internet public opinion regulation” and “dynamic judicial supervision”. The findings reveal that citizens exhibit higher trust in ADM is aimed to supervise authority rather than the public. Furthermore, citizens with greater algorithmic experience are more willing to trust ADM. The study identifies algorithmic black‐box and algorithmic flaws as mediators between algorithmic targets and citizens' trust in ADM. These insights provide practical policy recommendations for fostering public confidence in ADM
Corporate governance · Empirical evidence · Empirical research · Judicial opinion · Public opinion · Public trust · The Internet · E-Government and Public Services · Ethics and Social Impacts of AI · Social Media and Politics
Media Technologies
Trust in Automation
Trust in Automation
Assessing IT Usage
Reconsidering the Rational Public
Taking Advice
Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models
Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology
Black box algorithms and the rights of individuals
In AI we trust? Citizen perceptions of AI in government decision making
The importance of effectiveness versus transparency and stakeholder involvement in citizens’ perception of public sector algorithms
A three-dimensional framework of perceiving privacy
Facts versus fears
Marx and Digital Machines
Transparency
The European Union general data protection regulation
A Protection Motivation Theory of Fear Appeals and Attitude Change1
Facets of algorithmic literacy
Will Algorithms Blind People? The Effect of Explainable AI and Decision-Makers’ Experience on AI-supported Decision-Making in Government
The challenges and limits of big data algorithms in technocratic governance
Artificial intelligence in public services
What type of algorithm is perceived as fairer and more acceptable? A comparative analysis of rule-driven versus data-driven algorithmic decision-making in public affairs
Why do citizens support algorithmic government
Behavioral Public Administration
Institutional factors driving citizen perceptions of AI in government
Assessing public value failure in government adoption of artificial intelligence
Algorithms in the public sector. Why context matters
Citizen participation and the democratization of policy expertise
Beyond surveillance capitalism
Understanding perception of algorithmic decisions
Trust in Public Policy Algorithms
Algorithmic regulation
Algorithmic governance
Explaining Why the Computer Says No
Platform Government in the Era of Smart Technology
Years of Transparency Research
Human–AI Interactions in Public Sector Decision Making
Not Too Much, Not Too Little
Politicians, Bureaucrats, and Asymmetric Information
Data science, artificial intelligence and the third wave of digital era governance
Municipal surveillance regulation and algorithmic accountability
Transparency you can trust
| Highly cited | No |
|---|