亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Explainable AI improves task performance in human–AI collaboration

计算机科学 任务(项目管理) 人工智能 黑匣子 领域(数学分析) 机器学习 工厂(面向对象编程) 人机交互 数学 数学分析 经济 管理 程序设计语言
作者
Julian Senoner,Simon Schallmoser,Bernhard Kratzwald,Stefan Feuerriegel,Torbjørn H. Netland
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:14 (1): 31150-31150 被引量:53
标识
DOI:10.1038/s41598-024-82501-9
摘要

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human-AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed [Formula: see text] assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed [Formula: see text] assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, [Formula: see text]) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, [Formula: see text]) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.
最长约 10秒,即可获得该文献文件

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
宁宁发布了新的文献求助10
2秒前
酷波er应助科研通管家采纳,获得30
3秒前
千千方方123完成签到 ,获得积分10
15秒前
Great小飞侠完成签到,获得积分10
53秒前
科研通AI6.4应助Great小飞侠采纳,获得10
59秒前
清玄关注了科研通微信公众号
1分钟前
1分钟前
凝冬完成签到,获得积分20
1分钟前
jiang完成签到,获得积分10
1分钟前
NexusExplorer应助科研通管家采纳,获得10
2分钟前
科研通AI2S应助cy0824采纳,获得10
2分钟前
是是是完成签到,获得积分10
3分钟前
3分钟前
ASRI12349发布了新的文献求助10
3分钟前
酷波er应助科研通管家采纳,获得10
4分钟前
科研通AI2S应助科研通管家采纳,获得10
4分钟前
ASRI12349完成签到,获得积分10
4分钟前
iitj完成签到,获得积分10
4分钟前
田様应助坚定的鹭洋采纳,获得10
4分钟前
zhangnj发布了新的文献求助10
4分钟前
5分钟前
zhangnj发布了新的文献求助10
5分钟前
5分钟前
5分钟前
cy0824发布了新的文献求助10
5分钟前
Hello应助荷兰香猪采纳,获得10
5分钟前
5分钟前
5分钟前
荷兰香猪发布了新的文献求助10
5分钟前
荷兰香猪完成签到,获得积分10
5分钟前
6分钟前
zhangnj完成签到,获得积分10
6分钟前
6分钟前
6分钟前
nowss完成签到,获得积分10
7分钟前
7分钟前
Kao应助普鲁斯特采纳,获得10
7分钟前
7分钟前
7分钟前
7分钟前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(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
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
Dynamische Polarisation von H-1 und B-11 in (CH-3)-3NBH-3 500
CLSI M07 2024 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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7247708
求助须知:如何正确求助?哪些是违规求助? 8870700
关于积分的说明 18712113
捐赠科研通 6925926
什么是DOI,文献DOI怎么找? 3197998
关于科研通互助平台的介绍 2373767
邀请新用户注册赠送积分活动 2172861