Is diverse and inclusive AI trapped in the gap between reality and algorithmizability?
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
We investigate the preconditions of an operationalization of ethics on the example algorithmization, i.e. the mathematical implementation, of the concepts of fairness and diversity in AI. From a non-technical point of view in ethics, this implementation entails two major drawbacks, (1) as it narrows down big concepts to a single model that is deemed manageable, and (2) as it hides unsolved problems of humanity in a system that could be mistaken as the 'solution' to these problems. We encourage extra caution when dealing with such issues and vote for human oversight.
Details
| Original language | English |
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| Title of host publication | Proceedings of the 5th Northern Lights Deep Learning Conference ({NLDL}) |
| Pages | 75-80 |
| Publication status | Published - 2024 |
| Peer-reviewed | Yes |
Publication series
| Series | Proceedings of Machine Learning Research |
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| Volume | 233 |
Conference
| Title | 5th Northern Lights Deep Learning Conference |
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| Abbreviated title | NLDL 2024 |
| Conference number | 5 |
| Duration | 9 - 11 January 2024 |
| Location | UiT The Arctic University of Norway |
| City | Tromsø |
| Country | Norway |
External IDs
| ORCID | /0000-0002-6505-3563/work/191531463 |
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