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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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About me
About me
Posts
blog-posts
portfolio
preprints
publications
A New AI Lexicon: Power
Published in , 1900
Recommended citation: https://ainowinstitute.org/publication/a-new-ai-lexicon-power
A Guiding Framework for Vetting Technology Vendors Operating in the Public Sector
Published in , 1900
Recommended citation: 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. https://www.fordfoundation.org/work/learning/research-reports/a-guiding-framework-to-vetting-public-sector-technology-vendors/
Successes and struggles with computational reproducibility: Lessons from the Fragile Families Challenge
Published in Socius 2019, 1900
Recommended citation: D. Liu and M. Salganik. Successes and struggles with computational reproducibility: Lessons from the Fragile Families Challenge. Socius 5, (2019): 1-21. https://journals.sagepub.com/doi/10.1177/2378023119849803
RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity
Published in AIES 2021, 1900
Recommended citation: 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. https://dl.acm.org/doi/10.1145/3461702.3462618
Examining Responsibility and Deliberation in AI Impact Statements and Ethics Reviews
Published in AIES 2022, 1900
Recommended citation: 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. https://dl.acm.org/doi/10.1145/3514094.3534155
BiaScope: Visual Unfairness Diagnosis for Graph Embeddings
Published in VDS 2022 at IEEE VIS, 1900
Recommended citation: 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). https://ieeexplore.ieee.org/document/9980579
STABLE: Identifying and Mitigating Instability in Embeddings of the Degenerate Core
Published in SDM 2023, 1900
Recommended citation: D. Liu and T. Eliassi-Rad. 2023. Identifying and Mitigating Instability in Embeddings of the Degenerate Core. SIAM SDM'23. /files/papers/stable/stable-Main-SDM-23.pdf
Group fairness without demographics using social networks
Published in FAccT 2023, 1900
Recommended citation: D. Liu, V. Do, N. Usunier, M. Nickel. 2023. Group fairness without demographics using social networks. FAccT'23. https://arxiv.org/abs/2305.11361
When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations
Published in FAccT 2025, 1900
Recommended citation: D. Liu, J. Baek, T. Eliassi-Rad. 2025. When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations. FAccT'25 https://arxiv.org/abs/2310.09687
Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings
Published in KDD 2025, 1900
Recommended citation: D. Liu, A. Seshadri, T. Eliassi-Rad, J. Ugander. 2025. Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings. KDD'25 https://arxiv.org/abs/2405.00172
Identifying and Upweighting Power-Niche Users to Mitigate Popularity Bias in Recommendations
Published in arXiv 2025, 1900
Recommended citation: https://arxiv.org/abs/2509.17265
talks
teaching
DS 4400: Machine Learning and Data Mining I
Undergraduate course, Northeastern University, 1900