TY - GEN
T1 - CrowdIDEA
T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
AU - Yen, Chi Hsien
AU - Cheng, Haocong
AU - Xia, Yilin
AU - Huang, Yun
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/4/19
Y1 - 2023/4/19
N2 - Causal reasoning is crucial for people to understand data, make decisions, or take action. However, individuals often have blind spots and overlook alternative hypotheses, and using only data is insufficient for causal reasoning. We designed and implemented CrowdIDEA, a novel tool consisting of a three-panel integration incorporating the crowd's beliefs (Crowd Panel with two designs), data analytics (Data Panel), and user's causal diagram (Diagram Panel) to stimulate causal reasoning. Through an experiment with 54 participants, we showed the significant effects of the Crowd Panel designs on the outcomes of causal reasoning, such as an increased number of causal beliefs generated. Participants also devised new strategies for bootstrapping, strengthening, deepening, and explaining their causal beliefs, as well as taking advantage of the unique characteristics of both qualitative and quantitative data sources to reduce potential biases in reasoning. Our work makes theoretical and design implications for exploratory causal reasoning.
AB - Causal reasoning is crucial for people to understand data, make decisions, or take action. However, individuals often have blind spots and overlook alternative hypotheses, and using only data is insufficient for causal reasoning. We designed and implemented CrowdIDEA, a novel tool consisting of a three-panel integration incorporating the crowd's beliefs (Crowd Panel with two designs), data analytics (Data Panel), and user's causal diagram (Diagram Panel) to stimulate causal reasoning. Through an experiment with 54 participants, we showed the significant effects of the Crowd Panel designs on the outcomes of causal reasoning, such as an increased number of causal beliefs generated. Participants also devised new strategies for bootstrapping, strengthening, deepening, and explaining their causal beliefs, as well as taking advantage of the unique characteristics of both qualitative and quantitative data sources to reduce potential biases in reasoning. Our work makes theoretical and design implications for exploratory causal reasoning.
KW - Causal Diagrams
KW - Causal Reasoning
KW - Crowd Intelligence
KW - Crowd-informed Reasoning Tools
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85160015669&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85160015669&partnerID=8YFLogxK
U2 - 10.1145/3544548.3581021
DO - 10.1145/3544548.3581021
M3 - Conference contribution
AN - SCOPUS:85160015669
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 23 April 2023 through 28 April 2023
ER -