Publications

D. Liu, V. Do, N. Usunier, M. Nickel. 2023. Group fairness without demographics using social networks. FAccT'23. [pdf] [video]

D. Liu and T. Eliassi-Rad. 2023. Identifying and Mitigating Instability in Embeddings of the Degenerate Core. SIAM SDM'23. [Project Site] [pdf]

A. Rissaki*, B. Scarone*, D. Liu, A. Pandey, B. Klein, T. Eliassi-Rad, and M.A. Borkin. 2022. BiaScope: Visual Unfairness Diagnosis for Graph Embeddings. Symposium on Visualization in Data Science at IEEE VIS (VDS '22). [pdf]

D. Liu*, P. Nanayakkara*, S. Sakha, G. Abuhamad, S.L. Blodgett, N. Diakopoulos, J. Hullman, and T. Eliassi-Rad. Examining Responsibility and Deliberation in AI Impact Statements and Ethics Reviews. AIES'22. [pdf] [video]

D. Liu, Z. Shafi, W. Fleisher, T. Eliassi-Rad, and S. Alfeld. RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity. AIES'21. [pdf] [video] [code]

D. Liu and M. Salganik. Successes and struggles with computational reproducibility: Lessons from the Fragile Families Challenge. Socius 5, (2019): 1-21. [pdf]

D. Liu, A. Seshadri, T. Eliassi-Rad, J. Ugander. 2024. Re-visiting Skip-Gram Negative Sampling: Dimension Regularization for More Efficient Dissimilarity Preservation in Graph Embeddings. [pdf]

D. Liu, J. Baek, T. Eliassi-Rad. 2023. When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations. [pdf]

C. Conti-Cook, R. Pakzad, S. A. Sakha, and D. Liu. A Guiding Framework for Vetting Technology Vendors Operating in the Public Sector. 2023. A research report by the Ford Foundation. [link]

D. Liu and S. Sakha. A New AI Lexicon: Power. 2021. An essay contribution to AI Now's AI Lexicon project. [link]