Paging Dr. JARVIS! Will people accept advice from artificial intelligence for consequential risk management decisions?

建议(编程) 背景(考古学) 心理学 决策辅助工具 精算学 风险管理 应用心理学 业务 计算机科学 医学 财务 替代医学 生物 病理 古生物学 程序设计语言
作者
Connor Larkin,Caitlin Drummond,Joseph Árvai
出处
期刊:Journal of Risk Research [Routledge]
卷期号:25 (4): 407-422 被引量:21
标识
DOI:10.1080/13669877.2021.1958047
摘要

Artificial intelligence (AI), a branch of computer science based upon algorithms that can analyze data and make decisions autonomously, is becoming increasingly prevalent in the technology that powers modern society. Relatively little research has examined how humans modify their judgments in response to their interactions with AI. Our research explores how people respond to different types of risk management advice received from AI vs. a human expert in two contexts where AI is commonly deployed: medicine and finance. Through online studies with representative samples of Americans, we first find that participants generally prefer to receive medical and financial risk management advice from humans over AI. In two follow-up studies, we presented participants with a hypothetical medical or financial risk and asked them to make an initial decision—to address the risk immediately or to wait for more information—and to rate their confidence in this decision. Next, participants were informed that either a human expert or AI had analyzed their case and recommended either immediate risk management action or a wait-and-see approach. Participant then made a final decision using the same response scale as before. We compared participants' initial and final decisions, examining the extent to which participants updated their decisions upon receiving their recommendation as a function of the recommendation itself and its source. We find that participants updated their decisions to a greater degree in response to recommendations from human experts as compared to AI, but the magnitude of this effect differed by context.Supplemental data for this article is available online at https://doi.org/10.1080/13669877.2021.1958047 .

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
6aff完成签到,获得积分10
刚刚
derrick完成签到,获得积分20
1秒前
xiwke发布了新的文献求助10
1秒前
1秒前
1秒前
1秒前
000完成签到 ,获得积分10
1秒前
Leone完成签到,获得积分10
2秒前
2秒前
2秒前
粥粥完成签到,获得积分20
2秒前
汤圆有奶瓶完成签到,获得积分10
2秒前
充电宝应助三冬四夏采纳,获得10
2秒前
哆啦小孟发布了新的文献求助10
2秒前
云里雾里看花花不语完成签到,获得积分20
3秒前
阿曼尼发布了新的文献求助10
3秒前
充电宝应助龙仔采纳,获得10
3秒前
ljh024完成签到,获得积分10
3秒前
4秒前
4秒前
爱吃百香果完成签到,获得积分20
4秒前
温柔雪青发布了新的文献求助10
4秒前
Korai完成签到 ,获得积分10
5秒前
每天都想退学完成签到,获得积分10
5秒前
SSSS完成签到,获得积分10
5秒前
慕青应助misaaaa采纳,获得10
5秒前
jackwang完成签到,获得积分10
5秒前
小蘑菇应助小菜一碟2021采纳,获得10
5秒前
5秒前
Cker完成签到,获得积分10
5秒前
orixero应助wangshenglin采纳,获得10
5秒前
党参完成签到,获得积分10
6秒前
wenjin完成签到,获得积分10
6秒前
6秒前
6秒前
小周完成签到,获得积分10
6秒前
木子李完成签到,获得积分10
6秒前
qq是小白发布了新的文献求助10
6秒前
海东南发布了新的文献求助10
6秒前
6秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Salmon nasal cartilage-derived proteoglycan complexes influence the gut microbiota and bacterial metabolites in mice 2000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1500
Cowries - A Guide to the Gastropod Family Cypraeidae 1200
ON THE THEORY OF BIRATIONAL BLOWING-UP 666
Signals, Systems, and Signal Processing 610
“美军军官队伍建设研究”系列(全册) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6384904
求助须知:如何正确求助?哪些是违规求助? 8197926
关于积分的说明 17338382
捐赠科研通 5438442
什么是DOI,文献DOI怎么找? 2876083
邀请新用户注册赠送积分活动 1852640
关于科研通互助平台的介绍 1697031