Holstein, K. & Doroudi, S. (to appear). Fairness and equity in learning analytics systems (FairLAK). To appear in Companion Proceedings of the Ninth International Learning Analytics & Knowledge Conference (LAK’19). ACM.
Cramer, H., Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudík, M., Wallach, H., Reddy, S., & Garcia-Gathright, J. (2019). Challenges of incorporating algorithmic fairness into industry practice. Tutorial at the ACM Conference on Fairness, Accountability, and Transparency (FAT* 2019). ACM.
Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudík, M., Wallach, H. (2018). Opportunities for machine learning research to support fairness in industry practice. In the 2018 Workshop on Critiquing and Correcting Trends in Machine Learning at the Conference on Neural Information Processing Systems (NeurIPS 2018). [pdf] [poster]
Holstein, K., McLaren, B. M., & Aleven, V. (2018). Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED’18). [pdf] *Best Paper Award*
Holstein, K. (2018). Towards Teacher-AI Hybrid Systems. In Companion Proceedings of the Eighth International Learning Analytics & Knowledge Conference. ACM. [pdf]