User, Agent, Subject, Spy

I gave this talk on March 5, 2020 at the Boise State Computing Seminar Series.

My Research

These papers provide more details on the research I presented. Many of them have accompanying code to reproduce the experiments and results.

Michael D. Ekstrand, Ion Madrazo Azpiazu, Katherine Landau Wright, and Maria Soledad Pera. 2018. Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users. In Proceedings of the ComplexRec 2018 Second Workshop on Recommendation in Complex Scenarios (ComplexRec ’18), at RecSys 2018. Cited 2 times.

Maria Soledad Pera, Katherine Wright, and Michael D. Ekstrand. 2018. Recommending Texts to Children with an Expert in the Loop. In Proceedings of the 2nd International Workshop on Children & Recommender Systems (KidRec ’18), at IDC 2018. DOI 10.18122/cs_facpubs/140/boisestate. Cited 1 times.

Mucun Tian and Michael D. Ekstrand. 2020. Estimating Error and Bias in Offline Evaluation Results. Short paper in Proceedings of the 2020 Conference Human Information Interaction and Retrieval (CHIIR ’20). ACM, 5 pp. DOI 10.1145/3343413.3378004. arXiv:2001.09455 [cs.IR]. NSF PAR 10146883. Acceptance rate: 47%.

Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:172–186. Acceptance rate: 24%. Cited 16 times.

Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring Author Gender in Book Rating and Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). ACM, pp. 242–250. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR]. Acceptance rate: 17.5%. Cited 25 times.

Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan. 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:35–47. Acceptance rate: 24%. Cited 12 times.

Projects

Funding

Other Work Cited

  • Green, B and Viljoen, S. 2020. Algorithmic Realism: Expanding the Boundaries of Algorithmic Thought. In Conference on Fairness, Accountability, and Transparency (FAT* ’20) doi:10.1145/3351095.3372840.
  • ACM Code of Ethics
  • Crawford, K. 2017. The Trouble with Bias. NIPS 2017 Keynote.
  • Robyn Speer. 2017. ConceptNet Numberbatch 17.04: better, less-stereotyped word vectors.
  • Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. 2012. Fairness Through Awareness. In (Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (pp. 214–226). New York, NY, USA: ACM. doi:10.1145/2090236.2090255
  • Burke, R. 2017. Multisided Fairness for Recommendation. arXiv [cs.CY]. arxiv:1707.00093.
  • Steck, H. 2018. Calibrated Recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018). doi:10.1145/3240323.3240372.
  • Biega, A.J., Gummadi, K. P., and Weikum, G. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, ACM. doi:10.1145/3209978.3210063.
  • Ashudeep Singh and Thorsten Joachims. 2018. Fairness of Exposure in Rankings. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18), ACM, New York, NY, USA, 2219–2228.
  • Alex Beutel, Ed H. Chi, Cristos Goodrow, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, and Lichan Hong. 2019. Fairness in Recommendation Ranking through Pairwise Comparisons. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3292500.3330745