Soul of a new machine
Self-learning algorithms in public administration
Bibliographic Data
| ID | 6149282 |
|---|---|
| Authors | Lasse Gerrits (0000-0002-7649-6001, Erasmus University Rotterdam, corresponding author) |
| Year | 2021 |
| Volume | 26 |
| Issue | 3 |
| Pages | 237-250 |
| Publication date | 2021-01-05 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Information Polity (JOURNAL) |
| Journal identifiers | ISSN: 1875-8754 • E-ISSN: 1570-1255 |
| Publisher | IOS Press (PUBLISHER • NL) |
| DOI | 10.3233/ip-200224 |
| OpenAlex | W3120360074 |
| Language | EN |
| Citations received | 5 |
| References cited | 58 |
Big data sets in conjunction with self-learning algorithms are becoming increasingly important in public administration. A growing body of literature demonstrates that the use of such technologies poses fundamental questions about the way in which predictions are generated, and the extent to which such predictions may be used in policy making. Complementing other recent works, the goal of this article is to open the machine’s black box to understand and critically examine how self-learning algorithms gain agency by transforming raw data into policy recommendations that are then used by policy makers. I identify five major concerns and discuss the implications for policy making
Agency (philosophy · Algorithm · Big data · Black box · Data mining · Epistemology · Political science · Public policy · Raw data · Social science · Sociology · Soul · Computer Science · E-Government and Public Services · Ethics and Social Impacts of AI · Law · Privacy, Security, and Data Protection · Artificial Intelligence
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| Unique citing works | 5 |
|---|---|
| Citations per year | 1,67 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |