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

Carina Geldhauser, Hermann Diebel-Fischer

Keywords: Machine learning, Ethics, Fairness, Diversification, Inclusion

TL;DR: An AI ethics paper on the operationalization of fairness and diversity in AI

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.

Submission Number: 57