Is Diverse and Inclusive AI Trapped in the Gap Between Reality and Algorithmizability?

MCML Authors

Carina Geldhauser

Dr.

* Former Member

→ Group Massimo Fornasier
 Applied Numerical Analysis

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.

inproceedings GD24

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NLDL 2024

Northern Lights Deep Learning Conference. Tromsø, Norway, Jan 09-11, 2024.

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Authors

C. Geldhauser • H. Diebel-Fischer

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Links

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In Collaboration

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Research Area

 A2 | Mathematical Foundations