The Effect of past Algorithmic Performance and Decision Significance on Algorithmic Advice Acceptance

背景(考古学) 计算机科学 建议(编程) 显著性检验 实证研究 关系(数据库) 心理学 应用心理学 数学 统计 数据挖掘 生物 古生物学 程序设计语言
作者
Melissa Saragih,Ben W. Morrison
出处
期刊:International Journal of Human-computer Interaction [Taylor & Francis]
卷期号:38 (13): 1228-1237 被引量:13
标识
DOI:10.1080/10447318.2021.1990518
摘要

This study aimed to investigate people’s willingness to accept algorithmic over human advice, under varying conditions of previous algorithmic performance and decision significance. We randomly presented hypothetical scenarios to 218 participants. Scenarios differed in relation to decision context (i.e., choices relating to taxi-routes, movies, restaurants, medical interventions, savings strategies, and bush fire evacuation), and within each scenario past algorithmic performance was also varied (equal, above average, or far greater than the human expert). Participants were asked to rate decision significance, and their likelihood of choosing the algorithmic advice over the human expert. Based on participants’ perceived decision significance, scenarios were classified as either low- or high-stakes. We tested for differences in participants’ ratings of algorithmic acceptance across levels of past performance and decision significance. Results revealed that as past accuracy and decision significance increased, the likelihood of algorithmic advice adoption also increased. An interaction between past accuracy and decision significance indicated increased algorithmic advice acceptance under conditions of far greater previous performance, in high-, compared to low-stakes scenarios. These findings are contrary to a large body of past research wherein people’s algorithm aversion persisted despite superior algorithmic performance and have implications to human-algorithm interaction and system design.
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