Perceptions of AI Bad Behavior: Variations on Discordant Non-Performance

TL;DR

This study explores how non-experts perceive AI bad behavior, revealing that perceptions are influenced by moral foundations and become more salient when specific behaviors are evaluated.

Contribution

It introduces a framework combining moral foundations, construal level, and moral dyadism to understand perceptions of AI bad behavior.

Findings

Bad behaviors are less salient when discussing AI generally.

Perceptions of bad behavior increase when specific AI actions are evaluated.

Variations in perceived bad behaviors align with moral foundations.

Abstract

Popular discourses are thick with narratives of generative AI's problematic functions and outcomes, yet there is little understanding of how non-experts consider AI activities to constitute bad behavior. This study starts to bridge that gap through inductive analysis of interviews with non-experts (N = 28) focusing on large-language models in general and their bad behavior, specifically. Results suggest bad behaviors are not especially salient when people discuss AI generally but the notion of AI behaving badly is easily engaged when prompted, and bad behavior becomes even more salient when evaluating specific AI behaviors. Types of observed behaviors considered bad mostly align with their inspiring moral foundations; across all observed behaviors, some variations on non-performance and social discordance were present. By scaffolding findings at the intersections of moral foundations theory, construal level theory, and moral dyadism, a tentative framework for considering AI bad behavior is proposed.