| 000 -LEADER |
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| fixed length control field |
01518nam a22001457a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220907b ||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Busuioc, Madalina |
| 245 ## - TITLE STATEMENT |
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| Title |
Accountable artificial intelligence: Holding algorithms to account |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Public Administration Review |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
81(5), Sep-Oct, 2021: p.825-836 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Artificial intelligence (AI) algorithms govern in subtle yet fundamental ways the way we live and are transforming our societies. The promise of efficient, low-cost, or “neutral” solutions harnessing the potential of big data has led public bodies to adopt algorithmic systems in the provision of public services. As AI algorithms have permeated high-stakes aspects of our public existence—from hiring and education decisions to the governmental use of enforcement powers (policing) or liberty-restricting decisions (bail and sentencing)—this necessarily raises important accountability questions: What accountability challenges do AI algorithmic systems bring with them, and how can we safeguard accountability in algorithmic decision-making? Drawing on a decidedly public administration perspective, and given the current challenges that have thus far become manifest in the field, we critically reflect on and map out in a conceptually guided manner the implications of these systems, and the limitations they pose, for public accountability.- Reproduced |
| 773 ## - HOST ITEM ENTRY |
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| Main entry heading |
Public Administration Review |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
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| Subject DIP |
ARTIFICIAL INTELLIGENCE |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Articles |
