Publications

In most cases, my collaborators and I choose to have students as first authors and then determine the authorship alphabetically.

2022

Amy Rechkemmer and Ming Yin. When Confidence Meets Accuracy: Exploring the Effects of Multiple Performance Indicators on Trust in Machine Learning Models. In Proceedings of the 40th ACM Conference on Human Factors in Computing Systems (CHI), New Orleans, LA, April 30 - May 6, 2022. [supplementary materials] Best Paper Award
Xinru Wang, Zhuoran Lu, and Ming Yin. Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making. In Proceedings of the ACM Web Conference (WWW), Online, April 2022.
Xiaoni Duan, Chien-Ju Ho, and Ming Yin. The Influences of Task Design on Crowdsourced Judgement: A Case Study of Recidivism Risk Evaluation. In Proceedings of the ACM Web Conference (WWW), Online, April 2022.
Guoyang Zhou, Vaneet Aggarwal, Ming Yin, and Denny Yu. A Computer Vision Approach for Estimating Lifting Load Contributors to Injury Risk. IEEE Transactions on Human-Machine Systems, 52(2), April 2022.
Chun-Wei Chiang and Ming Yin. Exploring the Effects of Machine Learning Literacy Interventions on Laypeople's Reliance on Machine Learning Models. In Proceedings of the 27th ACM Conference on Intelligent User Interfaces (IUI), Online, March 2022. [supplementary materials]

2021

Guoyang Zhou, Vaneet Aggarwal, Ming Yin, and Denny Yu. Video-based AI Decision Support System for Lifting Risk Assessment. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Online, October 2021.
Chien-Ju Ho and Ming Yin. Designing and Optimizing Cognitive Debiasing Strategies for Crowdsourcing Annotation. In Proceedings of the Workshop on Investigating and Mitigating Biases in Crowdsourced Data at the 24th ACM Conference on Computer Supported Cooperative Work (CSCW), Online, October 2021.
Abdullah Almaatouq, Mohammed Alsobay, Ming Yin, and Duncan J. Watts. Task Complexity Moderates Group Synergy. Proceedings of the National Adademy of Sciences (PNAS), 118(36), September 2021.
Meric Altug Gemalmaz and Ming Yin. Accounting for Confirmation Bias in Crowdsourced Label Aggregation. In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), Online, August 2021.
Amy Rechkemmer and Ming Yin. Exploring the Effects of Goal Setting When Training for Complex Crowdsourcing Tasks. In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), Online, August 2021. (Sister Conferences Track)
Chun-Wei Chiang and Ming Yin. You'd Better Stop! Understanding Human Reliance on Machine Learning Models under Covariate Shift. In Proceedings of the 13th ACM Web Science Conference (WebSci), Online, June 2021.
Zhuoran Lu and Ming Yin. Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks. In Proceedings of the 39th ACM Conference on Human Factors in Computing Systems (CHI), Yokohama, Japan, May 2021. [supplementary materials]
Xinru Wang and Ming Yin. Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making. In Proceedings of the 26th ACM International Conference on Intelligent User Interfaces (IUI), College Station, TX, April 2021. [supplementary materials]

2020

Amy Rechkemmer and Ming Yin. Motivating Novice Crowd Workers through Goal Setting: An Investigation into the Effects on Complex Crowdsourcing Task Training. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Hilversum, Netherlands, October 2020. Best Paper Award
Xiaoni Duan, Chien-Ju Ho, and Ming Yin. Does Exposure to Diverse Perspectives Mitigate Biases in Crowdwork? An Explorative Study. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Hilversum, Netherlands, October 2020.
Xiao Hu, Haobo Wang, Anirudh Vegesana, Somesh Dube, Kaiwen Yu, Gore Kao, Shuo-Han Chen, Yung-Hsiang Lu, George Thiruvathukal, and Ming Yin. Crowdsourcing Detection of Sampling Biases in Image Datasets. In Proceedings of the Web Conference (WWW), Taipei, April 2020.

2019

Keman Huang, Jinhui Yao, and Ming Yin. Understanding the Skill Provision in Gig Economy from a Network Perspective: A Case Study of Fiverr. In Proceedings of the ACM on Human-Computer Interaction: Computer-Support Cooperative Work and Social Computing (CSCW), Austin, TX, November 2019.
Xiao Hu, Haobo Wang, Somesh Dube, Anirudh Vegesana, Kaiwen Yu, Yung-Hsiang Lu, and Ming Yin. Discovering Biases in Image Datasets with the Crowd. In Proceedings of the 7th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Skamania Lodge, WA, October 2019. (Work in Progress track)
Wei Tang, Chien-Ju Ho, and Ming Yin. Leveraging Peer Communication to Enhance Crowdsourcing. In Proceedings of the Web Conference (WWW), San Francisco, CA, May 2019.
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach. Understanding the Effect of Accuracy on Trust in Machine Learning Models. In Proceedings of the 37th ACM Conference on Human Factors in Computing Systems (CHI), Glasgow, UK, May 2019. Best Paper Honorable Mention

2018

Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach. Does Stated Accuracy Affect Trust in Machine Learning?. In Proceedings of the 3rd ICML Workshop on Human Interpretability in Machine Learning (WHI), Stockholm, Sweden, July 2018.
Ming Yin, Siddharth Suri, and Mary L. Gray. Running Out of Time: The Impact and Value of Flexibility in On-Demand Crowdwork. In Proceedings of the 36th ACM Conference on Human Factors in Computing Systems (CHI), Montreal, Canada, April 2018.

2017 and earlier

Ming Yin. Peeking into the On-Demand Economy: Crowd Behavior and Incentive Design. Ph.D. Dissertation, School of Engineering and Applied Sciences, Harvard University, June 2017.
Ming Yin and Yiling Chen. Predicting Crowd Work Quality under Monetary Interventions. In Proceedings of the 4th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Austin, TX, October 30 - November 3, 2016. [slides, dataset]
Edith Law, Ming Yin, Joslin Goh, Kevin Chen, Michael Terry, and Krzysztof Z. Gajos. Curiosity Killed the Cat, but Makes Crowdwork Better. In Proceedings of the 34th ACM Conference on Human Factors in Computing Systems (CHI), San Jose, CA, May 2016. Best Paper Honorable Mention
Ming Yin, Mary L. Gray, Siddharth Suri, and Jennifer Wortman Vaughan. The Communication Network Within the Crowd. In Proceedings of the 25th International World Wide Web Conference (WWW), Montreal, Canada, April 2016. [slides, dataset]
Ming Yin and Yiling Chen. Bonus or Not? Learn to Reward in Crowdsourcing. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, July 2015. [slides, poster]
Ming Yin and Yu-An Sun. Human Behavior Models for Virtual Agents in Repeated Decision Making under Uncertainty. In Proceedings of the 14th International Conference on Autonomous Agents & Multiagent Systems (AAMAS), Istanbul, Turkey, May 2015. [slides]
Ming Yin, Yiling Chen, and Yu-An Sun. Monetary Interventions in Crowdsourcing Task Switching. In Proceedings of the 2nd AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Pittsburgh, PA, November 2014. [slides]
Ming Yin, Yiling Chen, and Yu-An Sun. Task Sequence Design: Evidence on Price and Difficulty. In Proceedings of the 1st AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Palm Springs, CA, November 2013. (Work in Progress track) [slides, poster]
Ming Yin, Yiling Chen, and Yu-An Sun. The Effects of Performance-Contingent Financial Incentives in Online Labor Markets. In Proceedings of the 27th Conference on Artificial Intelligence (AAAI), Bellevue, WA, July 2013. [slides, poster]

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