Rethinking AI for Good Governance
Bibliographic Data
| ID | 9685797 |
|---|---|
| Authors | Helen Z Margetts (0000-0003-4597-8283, corresponding author), Helen Margetts |
| Year | 2022 |
| Volume | 151 |
| Issue | 2 |
| Pages | 360-371 |
| Publication date | 2022-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Daedalus (JOURNAL) |
| Journal identifiers | ISSN: 0011-5266 • E-ISSN: 1548-6192 |
| Publisher | MIT Press (PUBLISHER • US) |
| DOI | 10.1162/daed_a_01922 |
| OpenAlex | W4226076751 |
| Language | EN |
| Citations received | 11 |
| References cited | 3 |
This essay examines what AI can dofor government, specifically through three generic tools at the heart of governance: detection, prediction, and data-driven decision-making. Public sector functions, such as resource allocation and the protection of rights, are more normatively loaded than those of firms, and AI poses greater ethical challenges than earlier generations of digital technology, threatening transparency, fairness, and accountability. The essay discusses how AI might be developed specifically for government, with a public digital ethos to protect these values. Three moves that could maximize the transformative possibilities for a distinctively public sector AI are the development of government capacity to foster innovation through AI; the building of integrated and generalized models for policy-making; and the detection and tackling of structural inequalities. Combined, these developments could offer a model of data-intensive government that is more efficient, ethical, fair, prescient, and resilient than ever before in administrative history
Accountability · Business · Corporate governance · Economics · Ethos · Government (linguistics · Law and economics · Management · Political science · Public sector · Sociology · Transformative learning · Transparency (behavior · Law · Legal and Policy Issues · Public Administration
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| Unique citing works | 11 |
|---|---|
| Citations per year | 3,67 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 11 |