Zhiwei Wu

Stapleton, L., Lee, M. H., Qing, D., Wright, M., Chouldechova, A., Holstein, K., … Zhu, H. (2022). Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. In 2022 ACM Conference on Fairness, Accountability, and Transparency (p. 16). http://doi.org/10.1145/3531146.3533177

Deng, W. H., Nagireddy, M., Lee, M. S., Singh, J., Wu, Z. S., Holstein, K., & Zhu, H. (2022). Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. In 2022 ACM Conference on Fairness, Accountability, and Transparency (p. 12). http://doi.org/10.1145/3531146.3533113

Kawakami, A., Sivaraman, V., Stapleton, L., Cheng, H.-F., Perer, A., Wu, Z. S., … Holstein, K. (2022). “Why Do I Care What’s Similar?” Probing Challenges in AI-Assisted Child Welfare Decision-Making through Worker-AI Interface Design Concepts. In Designing Interactive Systems Conference (p. 17). http://doi.org/10.1145/3532106.3533556

Kawakami, A., Sivaraman, V., Cheng, H.-F., Stapleton, L., Cheng, Y., Qing, D., … Holstein, K. (2022). Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (p. 18). http://doi.org/10.1145/3491102.3517439

Cheng, H.-F., Stapleton, L., Kawakami, A., Sivaraman, V., Cheng, Y., Qing, D., … Zhu, H. (2022). How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (p. 22). http://doi.org/10.1145/3491102.3501831