Introduction to special issue algorithmic transparency in government
Towards a multi-level perspective
Dados Bibliográficos
| ID | 6149106 |
|---|---|
| Editores | Sarah Giest (0000-0001-8201-6943), Stephan Grimmelikhuijsen (0000-0002-1553-6065) |
| Ano | 2020 |
| Volume | 25 |
| Fascículo | 4 |
| Páginas | 409-417 |
| Data de publicação | 2020-11-25 |
| Peer Reviewed | Sim |
| Open Access | Não |
| Tipo | ARTICLE |
| Periódico | Information Polity (JOURNAL) |
| Identificadores do periódico | ISSN: 1875-8754 • E-ISSN: 1570-1255 |
| Editora | IOS Press (PUBLISHER • NL) |
| DOI | 10.3233/ip-200010 |
| OpenAlex | W3107681236 |
| Idioma | EN |
| Citações recebidas | 15 |
| Referências citadas | 32 |
The editorial sets the stage for the special issue on algorithmic transparency in government. The papers in the issue bring together transparency challenges experienced across different levels of government, including macro-, meso-, and micro-levels. This highlights that transparency issues transcend different levels of government – from European regulation to individual public bureaucrats. With a special focus on these links, the editorial sketches a future research agenda for transparency-related challenges. Highlighting these linkages is a first step towards seeing the bigger picture of why transparency mechanisms are put in place in some scenarios and not in others. Finally, this introduction present an agenda for future research, which opens the door to comparative analyses for future research and new insights for policymakers
Focus (optics · Government (linguistics · Open government · Perspective (graphical · Political science · Public relations · Transparency (behavior · Computer Science · E-Government and Public Services · Ethics and Social Impacts of AI · Law · Legal and Policy Issues · Public Administration · Artificial Intelligence
Exploring artificial intelligence adoption in public organizations
Algorithmic Transparency and Citizen Trust in Digital Governance
How rationale and process transparency shape perceived legitimacy in AI-assisted decisions
Navigating the Algorithmic State
Legitimacy of Algorithmic Decision-Making
Artificial Intelligence and Legal Transparency
When citizens meet the chatbot
Coping with digital transformation in frontline public services
Technology 3.0
Just like I thought’
A Replication of “Explaining Why the Computer Says No
Explaining Why the Computer Says No
Public Encounters and Government Chatbots
Human–AI Interactions in Public Sector Decision Making
A critical analysis of the study of gender and technology in government
Human Decisions and Machine Predictions
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
Big Data's Disparate Impact
How Much Information?
Automated Discretion
Rethink government with AI
To be or not to be algorithm aware
Fair, Transparent, and Accountable Algorithmic Decision-making Processes
Microbrook, Mesobrook, Macrobrook
Artificial Discretion as a Tool of Governance
Datafication and the Welfare State
Latent transparency and trust in government
Rationality and politics of algorithms. Will the promise of big data survive the dynamics of public decision making
Big and Open Linked Data (Bold) in government
Bridging Levels of Public Administration
Public Administration Challenges in the World of AI and Bots
For good measure’
A machine learning approach to open public comments for policymaking
Understanding the Complex Dynamics of Transparency
Accountable Artificial Intelligence
Machine justice
Administration by algorithm
Big Data and AI – A transformational shift for government
Critical Questions for Big Data
Toward an Ethics of Algorithms
Understanding the promises and premises of online health platforms
The ethics of algorithms
How the machine 'thinks
| Obras citantes distintas | 15 |
|---|---|
| Citações por ano | 3 |
| Intervalo de citações | 2021 - 2026 (6) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 15 |