TY - GEN
T1 - Workshop on Understanding and Mitigating Cognitive Biases in Human-AI Collaboration
AU - Boonprakong, Nattapat
AU - He, Gaole
AU - Gadiraju, Ujwal
AU - Van Berkel, Niels
AU - Wang, Danding
AU - Chen, Si
AU - Liu, Jiqun
AU - Tag, Benjamin
AU - Goncalves, Jorge
AU - Dingler, Tilman
PY - 2023
Y1 - 2023
N2 - AI systems are increasingly incorporated into human decision-making. Yet, human decision-makers are often affected by their cognitive biases. In critical settings, such as medical diagnosis, criminal judgment, or information consumption, these cognitive biases hinder optimal decision outcomes, thereby resulting in dangerous decisions and negative societal impact. The use of AI systems can amplify and exacerbate cognitive biases in their users. In this workshop, we seek to foster discussions on ongoing research around cognitive biases in human-AI collaboration and identify future research directions to understand, quantify, and mitigate the effects of cognitive biases. We will explore cognitive biases appearing in various contexts of human-AI collaboration: what can cause them?; how can we measure, model, mitigate, and manage cognitive biases?; and how can we utilise cognitive biases for the greater good? We will reflect on workshop discussions to form a research community around cognitive biases and bias-aware systems.
AB - AI systems are increasingly incorporated into human decision-making. Yet, human decision-makers are often affected by their cognitive biases. In critical settings, such as medical diagnosis, criminal judgment, or information consumption, these cognitive biases hinder optimal decision outcomes, thereby resulting in dangerous decisions and negative societal impact. The use of AI systems can amplify and exacerbate cognitive biases in their users. In this workshop, we seek to foster discussions on ongoing research around cognitive biases in human-AI collaboration and identify future research directions to understand, quantify, and mitigate the effects of cognitive biases. We will explore cognitive biases appearing in various contexts of human-AI collaboration: what can cause them?; how can we measure, model, mitigate, and manage cognitive biases?; and how can we utilise cognitive biases for the greater good? We will reflect on workshop discussions to form a research community around cognitive biases and bias-aware systems.
KW - Cognitive Bias
KW - Debiasing
KW - Human-AI Collaboration
UR - http://www.scopus.com/inward/record.url?scp=85176214108&partnerID=8YFLogxK
U2 - 10.1145/3584931.3611284
DO - 10.1145/3584931.3611284
M3 - Conference contribution
AN - SCOPUS:85176214108
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 512
EP - 517
BT - CSCW 2023 Companion - Conference Companion Publication of the 2023 Computer Supported Cooperative Work and Social Computing
A2 - Ames, Morgan
A2 - Fussell, Susan
A2 - Gilbert, Eric
A2 - Liao, Vera
A2 - Ma, Xiaojuan
A2 - Page, Xinru
A2 - Rouncefield, Mark
A2 - Singh, Vivek
A2 - Wisniewski, Pamela
PB - Association for Computing Machinery (ACM)
T2 - 26th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2023
Y2 - 14 October 2023 through 18 October 2023
ER -