Following wrong suggestions: self-blame in human and computer scenarios

TL;DR

This study explores how people experience self-blame after following wrong suggestions from humans or machines, revealing reduced responsibility attribution to machines and highlighting emotional impacts on human-machine cooperation.

Contribution

It provides novel insights into emotional responses and responsibility perception after following incorrect suggestions from AI compared to humans.

Findings

Responsibility perception decreases when following machine suggestions.

Negative emotions are triggered by wrong outcomes from AI suggestions.

Implications for improving human-AI cooperation and trust.

Abstract

This paper investigates the specific experience of following a suggestion by an intelligent machine that has a wrong outcome and the emotions people feel. By adopting a typical task employed in studies on decision-making, we presented participants with two scenarios in which they follow a suggestion and have a wrong outcome by either an expert human being or an intelligent machine. We found a significant decrease in the perceived responsibility on the wrong choice when the machine offers the suggestion. At present, few studies have investigated the negative emotions that could arise from a bad outcome after following the suggestion given by an intelligent system, and how to cope with the potential distrust that could affect the long-term use of the system and the cooperation. This preliminary research has implications in the study of cooperation and decision making with intelligent machines. Further research may address how to offer the suggestion in order to better cope with user's self-blame.