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Ming Yin
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Understanding the effect of accuracy on trust in machine learning models
M Yin, J Wortman Vaughan, H Wallach
Proceedings of the 2019 chi conference on human factors in computing systems …, 2019
4002019
Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making
X Wang, M Yin
26th international conference on intelligent user interfaces, 318-328, 2021
1602021
The communication network within the crowd
M Yin, ML Gray, S Suri, JW Vaughan
Proceedings of the 25th International Conference on World Wide Web, 1293-1303, 2016
1282016
Curiosity killed the cat, but makes crowdwork better
E Law, M Yin, J Goh, K Chen, MA Terry, KZ Gajos
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems …, 2016
1112016
The effects of performance-contingent financial incentives in online labor markets
M Yin, Y Chen, YA Sun
Proceedings of the AAAI Conference on Artificial Intelligence 27 (1), 1191-1197, 2013
732013
Human reliance on machine learning models when performance feedback is limited: Heuristics and risks
Z Lu, M Yin
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021
712021
Task complexity moderates group synergy
A Almaatouq, M Alsobay, M Yin, DJ Watts
Proceedings of the National Academy of Sciences 118 (36), e2101062118, 2021
512021
Bonus or not? learn to reward in crowdsourcing.
M Yin, Y Chen
IJCAI, 201-208, 2015
502015
When confidence meets accuracy: Exploring the effects of multiple performance indicators on trust in machine learning models
A Rechkemmer, M Yin
Proceedings of the 2022 chi conference on human factors in computing systems …, 2022
412022
Running out of time: The impact and value of flexibility in on-demand crowdwork
M Yin, S Suri, ML Gray
Proceedings of the 2018 CHI conference on human factors in computing systems …, 2018
402018
Monetary interventions in crowdsourcing task switching
M Yin, Y Chen, YA Sun
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 …, 2014
402014
You’d better stop! Understanding human reliance on machine learning models under covariate shift
CW Chiang, M Yin
Proceedings of the 13th ACM Web Science Conference 2021, 120-129, 2021
332021
Understanding the skill provision in gig economy from a network perspective: A case study of fiverr
K Huang, J Yao, M Yin
Proceedings of the ACM on Human-Computer Interaction 3 (CSCW), 1-23, 2019
312019
Leveraging peer communication to enhance crowdsourcing
W Tang, M Yin, CJ Ho
The World Wide Web Conference, 1794-1805, 2019
202019
Exploring the effects of machine learning literacy interventions on laypeople’s reliance on machine learning models
CW Chiang, M Yin
27th International Conference on Intelligent User Interfaces, 148-161, 2022
182022
Effects of explanations in ai-assisted decision making: Principles and comparisons
X Wang, M Yin
ACM Transactions on Interactive Intelligent Systems 12 (4), 1-36, 2022
172022
Accounting for Confirmation Bias in Crowdsourced Label Aggregation
MA Gemalmaz, M Yin
Proceedings of the 30th International Joint Conference on Artificial …, 2021
162021
Does exposure to diverse perspectives mitigate biases in crowdwork? an explorative study
X Duan, CJ Ho, M Yin
Proceedings of the aaai conference on human computation and crowdsourcing 8 …, 2020
162020
Crowdsourcing detection of sampling biases in image datasets
X Hu, H Wang, A Vegesana, S Dube, K Yu, G Kao, SH Chen, YH Lu, ...
Proceedings of The Web Conference 2020, 2955-2961, 2020
162020
Mechanism design for multi-slot ads auction in sponsored search markets
X Deng, Y Sun, M Yin, Y Zhou
Frontiers in Algorithmics: 4th International Workshop, FAW 2010, Wuhan …, 2010
162010
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