Jessie J. Smith, Michael Madaio, Robin Burke, and Casey Fiesler. 2025. Pragmatic Fairness: Evaluating ML Fairness Within the Constraints of Industry. In The 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’25), June 23–26, 2025, Athens, Greece. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3715275.3732040
🏆 Best Paper Award: Jessie J. Smith, Aishwarya Satwani, Robin Burke, and Casey Fiesler. 2024. Recommend Me? Designing Fairness Metrics with Providers. In FAccT ’24: ACM Conference on Fairness, Accountability and Transparency, June 03–06, 2024, Rio de Janeiro, Brazil. ACM, New York, NY, USA, 17 pages. https://doi.org/10.1145/3630106.3659044
Jessie J. Smith and Anas Buhayh, Anushka Kathait, Pradeep Ragothaman, Nicholas Mattei, Robin Burke, and Amy Voida. 2023. The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending Recommendation. In FAccT ’23: ACM Conference on Fairness, Accountability and Transparency, June 12–15, 2023, Chicago, IL. ACM, New York, NY, USA, 17 pages.
Jessie J. Smith, Lex Beattie, and Henriette Cramer. 2023. Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective. In Proceedings of the ACM Web Conference 2023 (WWW ’23), May 1–5, 2023, Austin, TX, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3543507.3583204
Jessie J. Smith, Blakeley H. Payne, Shamika Klassen, Dylan Thomas Doyle,
and Casey Fiesler. 2023. Incorporating Ethics in Computing Courses:
Barriers, Support, and Perspectives from Educators. In Proceedings of the
54th ACM Technical Symposium on Computing Science Education V. 1 (SIGCSE 2023), March 15–18, 2023, Toronto, ON, Canada. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3545945.3569855
Jessie J. Smith, Lex Beattie. 2022. RecSys Fairness Metrics: Many to Use But Which One to Choose? In FAccTRec Workshop at Sixteenth ACM Conference on Recommender Systems (RecSys '22), September 18-23, 2022, Seattle, WA, USA. ACM, NY, USA.
Jessie J. Smith, Lucia Jayne, and Robin Burke. 2022. Recommender Systems
and Algorithmic Hate. Late Breaking Results In Sixteenth ACM Conference on Recommender Systems (RecSys ’22), September 18–23, 2022, Seattle, WA, USA. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3523227.3551480
Jessie J. Smith. 2022. Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems. In Doctoral Symposium at Sixteenth ACM Conference on Recommender Systems (RecSys ’22), September 18–23, 2022, Seattle, WA, USA. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3523227.3547428
Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, and Jennifer Wortman Vaughan. REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research. In Proceedings of the 5th ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022.
Sonboli, Nasim and Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, and Casey Fiesler. "Fairness and Transparency in Recommendation: The Users' Perspective." In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, 274-279, 2021.
Fiesler, Casey, Mikhaila Friske, Natalie Garrett, Felix Muzny, Jessie J. Smith, and Jason Zietz. "Integrating Ethics into Introductory Programming Classes." In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE’21). New York, NY, USA: ACM. 2021.
Dylan Doyle-Burke and Jessie J. Smith. “What is Radical AI? Towards a Common Definition and our Collective Liberation.” Nov. 2020. Accepted into the Resistance AI Workshop at the Proceedings of NeurIPS 2020.
Smith, Jessie, et al. “Exploring User Opinions of Fairness in Recommender Systems.” ArXiv:2003.06461 [Cs], Mar. 2020. arXiv.org, Presented at the Fair and Responsible AI Workshop at The Proceedings of CHI 2020
Mansoury, M., Abdollahpouri H., Smith J., Dehpanah A., Pechenizkiy M., Mobasher, B. “Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems.” Proceedings of the Thirty-Third International Florida Artifical Intelligence Research Society Conference.
Pinter, A. T., Paul, J. M., Smith, J., & Brubaker, J. R. (2020). P4KxSpotify: A Dataset of Pitchfork Music Reviews and Spotify Musical Features. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 895-902.
Vito Walter Anelli, Amra Delić, Gabriele Sottocornola, Jessie Smith, Nazareno Andrade, Luca Belli, Michael Bronstein, Akshay Gupta, Sofia Ira Ktena, Alexandre Lung-Yut-Fong, Frank Portman, Alykhan Tejani, Yuanpu Xie, Xiao Zhu, and Wenzhe Shi. 2020. RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter’s Home Timeline. In Fourteenth ACM Conference on Recommender Systems (RecSys '20). Association for Computing Machinery, New York, NY, USA, 623–627.
Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fon, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael Bronstein, Amra Delić, Gabriele Sottocornola, Walter Anelli, Nazareno Andrade, Jessie Smith, Wenzhe Shi "Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline". arXiv:2004.13715 [Cs], April. 2020. arXiv.org.