| 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 | 460 | 2019 |
| 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 | 208 | 2021 |
| 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 | 136 | 2016 |
| 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 | 116 | 2016 |
| 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 | 86 | 2021 |
| 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 | 75 | 2013 |
| 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 | 59 | 2022 |
| Task complexity moderates group synergy A Almaatouq, M Alsobay, M Yin, DJ Watts Proceedings of the National Academy of Sciences 118 (36), e2101062118, 2021 | 54 | 2021 |
| Bonus or not? learn to reward in crowdsourcing. M Yin, Y Chen IJCAI, 201-208, 2015 | 54 | 2015 |
| 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 | 42 | 2018 |
| Monetary interventions in crowdsourcing task switching M Yin, Y Chen, YA Sun Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 …, 2014 | 42 | 2014 |
| 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 | 38 | 2021 |
| 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 | 36 | 2019 |
| Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making S Ma, Y Lei, X Wang, C Zheng, C Shi, M Yin, X Ma Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023 | 31 | 2023 |
| Leveraging peer communication to enhance crowdsourcing W Tang, M Yin, CJ Ho The World Wide Web Conference, 1794-1805, 2019 | 25 | 2019 |
| Will you accept the AI recommendation? Predicting human behavior in AI-assisted decision making X Wang, Z Lu, M Yin Proceedings of the ACM web conference 2022, 1697-1708, 2022 | 24 | 2022 |
| 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 | 24 | 2022 |
| 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 | 23 | 2022 |
| A holistic framework for analyzing the COVID-19 vaccine debate ML Pacheco, T Islam, M Mahajan, A Shor, M Yin, L Ungar, D Goldwasser arXiv preprint arXiv:2205.01817, 2022 | 20 | 2022 |
| Accounting for Confirmation Bias in Crowdsourced Label Aggregation MA Gemalmaz, M Yin Proceedings of the 30th International Joint Conference on Artificial …, 2021 | 18 | 2021 |
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York CityVerified email at microsoft.com
