A blindspot of AI ethics: anti-fragility in statistical prediction

TL;DR

This paper highlights the overlooked risk of anti-fragility loss in AI systems, arguing that over-optimization for short-term accuracy can harm societal diversity and adaptability.

Contribution

It introduces the concept of anti-fragility in AI ethics, emphasizing the need to consider societal impacts beyond trustworthiness and bias.

Findings

Over-optimization reduces societal diversity.

Current AI methods threaten societal flexibility.

Anti-fragility is crucial for societal progress.

Abstract

With this paper, we aim to put an issue on the agenda of AI ethics that in our view is overlooked in the current discourse. The current discussions are dominated by topics suchas trustworthiness and bias, whereas the issue we like to focuson is counter to the debate on trustworthiness. We fear that the overuse of currently dominant AI systems that are driven by short-term objectives and optimized for avoiding error leads to a society that loses its diversity and flexibility needed for true progress. We couch our concerns in the discourse around the term anti-fragility and show with some examples what threats current methods used for decision making pose for society.