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

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

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

OriginalspracheEnglisch
TitelProceedings of the 5th Northern Lights Deep Learning Conference ({NLDL})
Seiten75-80
PublikationsstatusVeröffentlicht - 2024
Peer-Review-StatusJa

Publikationsreihe

ReiheProceedings of Machine Learning Research
Band233

Konferenz

Titel5th Northern Lights Deep Learning Conference
KurztitelNLDL 2024
Veranstaltungsnummer5
Dauer9 - 11 Januar 2024
OrtUiT The Arctic University of Norway
StadtTromsø
LandNorwegen

Externe IDs

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