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

Carina Geldhauser, Hermann Diebel-Fischer
Proceedings of the 5th Northern Lights Deep Learning Conference ({NLDL}), PMLR 233:75-80, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v233-geldhauser24a, title = {Is diverse and inclusive {AI} trapped in the gap between reality and algorithmizability?}, author = {Geldhauser, Carina and Diebel-Fischer, Hermann}, booktitle = {Proceedings of the 5th Northern Lights Deep Learning Conference ({NLDL})}, pages = {75--80}, year = {2024}, editor = {Lutchyn, Tetiana and Ramírez Rivera, Adín and Ricaud, Benjamin}, volume = {233}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v233/geldhauser24a/geldhauser24a.pdf}, url = {https://proceedings.mlr.press/v233/geldhauser24a.html}, 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.} }
Endnote
%0 Conference Paper %T Is diverse and inclusive AI trapped in the gap between reality and algorithmizability? %A Carina Geldhauser %A Hermann Diebel-Fischer %B Proceedings of the 5th Northern Lights Deep Learning Conference ({NLDL}) %C Proceedings of Machine Learning Research %D 2024 %E Tetiana Lutchyn %E Adín Ramírez Rivera %E Benjamin Ricaud %F pmlr-v233-geldhauser24a %I PMLR %P 75--80 %U https://proceedings.mlr.press/v233/geldhauser24a.html %V 233 %X 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.
APA
Geldhauser, C. & Diebel-Fischer, H.. (2024). Is diverse and inclusive AI trapped in the gap between reality and algorithmizability?. Proceedings of the 5th Northern Lights Deep Learning Conference ({NLDL}), in Proceedings of Machine Learning Research 233:75-80 Available from https://proceedings.mlr.press/v233/geldhauser24a.html.

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