Is diverse and inclusive AI trapped in the gap between reality and algorithmizability?

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

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 languageEnglish
Title of host publicationProceedings of the 5th Northern Lights Deep Learning Conference ({NLDL})
Pages75-80
Publication statusPublished - 2024
Peer-reviewedYes

Publication series

SeriesProceedings of Machine Learning Research
Volume233

Conference

Title5th Northern Lights Deep Learning Conference
Abbreviated titleNLDL 2024
Conference number5
Duration9 - 11 January 2024
LocationUiT The Arctic University of Norway
CityTromsø
CountryNorway

External IDs

ORCID /0000-0002-6505-3563/work/191531463