Humble Machines: Attending to the Underappreciated Costs of Misplaced Distrust

TL;DR

This paper investigates how AI systems' focus on false positive reduction fosters distrust by neglecting false negatives, leading to miscategorization and eroding public trust, and advocates for 'humble trust' in AI design.

Contribution

It introduces a novel theory linking AI distrust to moral costs of false negatives and proposes designing systems with 'humble trust' to improve public confidence.

Findings

Distrust arises from concern over miscategorization of trustworthy individuals.

Systems prioritizing false positives increase false negatives, causing trust issues.

Implementing 'humble trust' can mitigate distrust and improve human-AI relationships.

Abstract

It is curious that AI increasingly outperforms human decision makers, yet much of the public distrusts AI to make decisions affecting their lives. In this paper we explore a novel theory that may explain one reason for this. We propose that public distrust of AI is a moral consequence of designing systems that prioritize reduction of costs of false positives over less tangible costs of false negatives. We show that such systems, which we characterize as 'distrustful', are more likely to miscategorize trustworthy individuals, with cascading consequences to both those individuals and the overall human-AI trust relationship. Ultimately, we argue that public distrust of AI stems from well-founded concern about the potential of being miscategorized. We propose that restoring public trust in AI will require that systems are designed to embody a stance of 'humble trust', whereby the moral costs of the misplaced distrust associated with false negatives is weighted appropriately during development and use.