Papers
In-progress manuscripts
Seung Hyun Cheon, Anneke Wernerfelt, Sorelle A. Friedler, Berk Ustun.
Feature Responsiveness Scores: model-agnostic explanations for recourse. arXiv:2410.2259, Oct. 29, 2024.
[link]
Oghenefejiro Isaacs Anigboro, Charlie M Crawford, Danae Metaxa, Sorelle A Friedler.
Identity-related Speech Suppression in Generative AI Content Moderation. arXiv:2409.13725, Sept. 9, 2024.
[link]
Dennis Robert Windham, Caroline J Wendt, Alex Crane, Sorelle A Friedler, Blair D Sullivan, Aaron Clauset.
Fast algorithms to improve fair information access in networks. arXiv:2409.03127, Sept. 4, 2024.
[link]
Ashkan Bashardoust, Hannah C. Beilinson, Sorelle A. Friedler, Jiajie Ma, Jade Rousseau, Carlos E. Scheidegger, Blair D. Sullivan, Nasanbayar Ulzii-Orshikh, Suresh Venkatasubramanian.
Information access representations and social capital in networks. arXiv:2010.12611, Oct. 16, 2023.
[link]
Peer-reviewed publications
Yaaseen Mahomed, Charlie M. Crawford, Sanjana Gautam, Sorelle A. Friedler, and Danae Metaxa.
Auditing GPT's Content Moderation Guardrails: Can ChatGPT Write Your Favorite TV Show?
Conference on Fairness, Accountability, and Transparency (FAccT), 2024. [PDF | link]
Xiaorong Wang, Clara Na, Emma Strubell, Sorelle Friedler, Sasha Luccioni.
Energy and Carbon Considerations of Fine-Tuning BERT. Conference on Empirical Methods in Natural Language Processing: Findings of EMNLP, 2023. [PDF | link]
Mohsen Abbasi, Calvin Barrett, Sorelle A. Friedler, Kristian Lum, Suresh Venkatasubramanian.
Measuring and mitigating voting access disparities: a study of race and polling locations in Florida
and North Carolina. Conference on Fairness, Accountability, and Transparency (FAccT), 2023. [PDF | link]
Ashkan Bashardoust, Sorelle A. Friedler, Carlos Scheidegger, Blair D. Sullivan and Suresh Venkatasubramanian.
Reducing Access Disparities in Networks using Edge Augmentation. Conference on
Fairness, Accountability, and Transparency (FAccT), 2023. [PDF | link]
Lydia Reader, Pegah Nokhiz, Cathleen Power, Neal Patwari, Suresh Venkatasubramanian, and
Sorelle A. Friedler. Models for understanding and quantifying feedback in societal systems. Conference
on Fairness, Accountability, and Transparency (FAccT), 2022. [PDF | link]
Venkateswaran Shekar, Gareth Nicholas, Mansoor Ani Najeeb, Margaret Zeile, Vincent Yu, Xiaorong
Wang, Dylan Slack, Zhi Li, Philip W. Nega, Emory Chan, Alexander J. Norquist, Joshua Schrier, and
Sorelle A. Friedler. Active Meta-Learning for Predicting and Selecting Perovskite
Crystallization Experiments. The Journal of Chemical Physics, Feb. 14, 2022. [PDF | link]
I. Elizabeth Kumar, Carlos Scheidegger, Suresh Venkatasubramanian, and Sorelle A. Friedler. Shapley
Residuals: Quantifying the limits of the Shapley value for explanations. In Neural Information
Processing Systems (NeurIPS), 2021. [PDF | link]
Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. The (im)possibility of
fairness: different value systems require different mechanisms for fair decision making. Communications
of the ACM, April, 2021. [PDF | link]
I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle A. Friedler. Problems with Shapley-value-based explanations as feature importance measures. In International Conference on Machine Learning (ICML), 2020. [PDF | link]
Dylan Slack, Sorelle A. Friedler, and Emile Givental. Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data. In Conference on Fairness, Accountability, and Transparency (FAccT), 2020. [PDF | link]
Xiwen Jia, Oscar Huang, Allyson Lynch, Matthew Danielson, Immaculate Lang’at, Alexander Milder, Aaron Ruby, Hao Wang, Sorelle A. Friedler, Alexander J. Norquist, and Joshua Schrier. Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis. Nature, 573: 251 - 255, Sept. 12, 2019. [PDF | link]
Harry Levin and Sorelle A. Friedler. Automated Congressional Redistricting. ACM Journal of Experimental Algorithmics, 24(1): 1-10, 2019. [PDF | link | code]
Ian M Pendleton, Gary Cattabriga, Zhi Li, Mansoor Ani Najeeb, Sorelle A Friedler, Alexander J Norquist, Emory M Chan, and Joshua Schrier.
Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management.
MRS Communications, 2019. [PDF | link]
Charles Marx, Richard Phillips, Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. Disentangling Influence: Using disentangled representations to audit model predictions. In Neural Information Processing Systems (NeurIPS), 2019. [PDF | link]
Benjamin Fish, Ashkan Bashardoust, danah boyd, Sorelle Friedler, Carlos Scheidegger and Suresh Venkatasubramanian. Gaps in Information Access in Social Networks. In The Web Conference (WWW), 2019. [PDF]
Mohsen Abbasi, Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. Fairness in representation: Quantifying stereotyping as a representational harm. In SIAM International Conference on Data Mining (SDM), 2019. [PDF]
Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, and Derek Roth. A comparative study of fairness-enhancing interventions in machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*), 2019. [PDF | code]
Andrew Selbst, danah boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet A. Vertesi. Fairness and Abstraction in Sociotechnical Systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*), 2019. [PDF | link]
Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger, Suresh Venkatasubramanian. Decision Making with Limited Feedback: Error bounds for Recidivism Prediction and Predictive Policing. In Algorithmic Learning Theory (ALT) 2018. [PDF | link]
Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger and Suresh Venkatasubramanian. Runaway Feedback Loops in Predictive Policing. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*), 2018. [PDF | link]
Richard L. Phillips, Kyu Hyun Chang, and Sorelle A. Friedler. Interpretable Active Learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*), 2018. [PDF | link]
Philip Adler, Casey Falk, Sorelle A. Friedler, Tionney Nix, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian. Auditing Black-box Models for Indirect Influence. Knowledge and Information Systems, 54(1): 95-122, 2018. [PDF | link | code]
Philip Adler, Casey Falk, Sorelle A. Friedler, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian. Auditing Black-box Models for Indirect Influence. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2016. [PDF | code]
Paul Raccuglia, Katherine C. Elbert, Philip D. F. Adler, Casey Falk, Malia B. Wenny, Aurelio Mollo, Matthias Zeller, Sorelle A. Friedler, Joshua Schrier, and Alexander J. Norquist. Machine-learning-assisted materials discovery using failed experiments. Nature, 533: 73 - 76, May 5, 2016. [PDF | link | project site]
F. Betul Atalay, Sorelle A. Friedler, and Dianna Xu. Convex hull for probabilistic points. In Technical Papers of the 29th Conference on Graphics, Patterns and Images (SIBGRAPI '16), 2016. [PDF]
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and Removing Disparate Impact. Proceedings of the 21st ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, 2015. [PDF | code]
Sorelle A. Friedler and David M. Mount. A Sensor-Based Framework for Kinetic Data Compression. Computational Geometry: Theory and Applications, 48(3): 147 - 168, March 2015. (doi: 10.1016/j.comgeo.2014.09.002) [PDF | link]
Sorelle A. Friedler and David M. Mount. Approximation algorithm for the kinetic robust k-center problem. Computational Geometry: Theory and Applications, 2010. (doi: 10.1016/j.comgeo.2010.01.001). [PDF (preprint) | link]
Sorelle A. Friedler and David M. Mount. Spatio-temporal range searching over compressed kinetic sensor data. In Proc. of the European Symposium on Algorithms (ESA), pages 386-397, 2010. [PDF | link] [TR]
2nd Workshop on Massive Data Algorithmics, 2010 [PDF]
Fall Workshop on Computational Geometry, 2009 [PDF]
Sorelle A. Friedler and David M. Mount. Compressing kinetic data from sensor networks. In Proc. of the 5th International Workshop on Algorithmic Aspects of Wireless Sensor Networks (AlgoSensors), pages 191 - 202, 2009. [PDF (preprint) | link] [TR]
Sorelle A. Friedler, Yee Lin Tan, Nir J. Peer, and Ben Shneiderman. Enabling teachers to explore grade patterns to identify individual needs and promote
fairer student assessment. Computers & Education, 51(4):1467-1485, December
2008. [PDF | link] [code and help videos]
Workshop Papers and Technical Reports
I. Elizabeth Kumar, Carlos Scheidegger, Suresh Venkatasubramanian, and Sorelle Friedler. Shapley Residuals: Quantifying the limits of the Shapley value for explanations. ICML Workshop on Workshop on Human Interpretability in Machine Learning (WHI), 2020. [link]
Dylan Slack, Sorelle Friedler and Emile Givental. Fairness Warnings. NeurIPS Workshop on Human-Centric Machine Learning, 2019. [link]
Dylan Slack, Sorelle Friedler and Emile Givental. Fair Meta-Learning: Learning How to Learn Fairly. NeurIPS Workshop on Human-Centric Machine Learning, 2019. [link]
Kadan Lottick, Silvia Susai, Sorelle Friedler, and Jonathan Wilson.
Energy Usage Reports: Environmental awareness as part of algorithmic accountability.
NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2019. [link]
Charles Marx, Richard Phillips, Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. Disentangling Influence: Using disentangled representations to audit model predictions. arXiv:1906.08652, Jun. 20, 2019. [link]
Dylan Slack, Sorelle A. Friedler, Chitradeep Dutta Roy, and Carlos Scheidegger. Assessing the Local Interpretability of Machine Learning Models. NeurIPS Workshop on Human-Centric Machine Learning, 2019. [link]
Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger and Suresh Venkatasubramanian. Runaway Feedback Loops in Predictive Policing. Presented as a talk at the Fairness, Accountability, and Transparency in Machine Learning Workshop, Aug. 14, 2017. [link]
Danielle Ensign, Sorelle Friedler, Scott Neville, Carlos Scheidegger and Suresh Venkatasubramanian. Decision Making with Limited Feedback: Error bounds for Recidivism Prediction and Predictive Policing. Presented as a poster at the Fairness, Accountability, and Transparency in Machine Learning Workshop, Aug. 14, 2017. [PDF]
Richard L. Phillips, Kyu Hyun Chang, and Sorelle A. Friedler. Interpretable Active Learning. Presented at the ICML Workshop on Human Interpretability in Machine Learning, Aug. 10, 2017.
Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. On the (im)possibility of fairness. arXiv:1609.07236, Sept. 23, 2016. [link]
Nicholas Diakopoulos, Sorelle Friedler, Marcelo Arenas, Solon Barocas, Michael Hay, Bill Howe, HV Jagadish, Kris Unsworth, Arnaud Sahuguet, Suresh Venkatasubramanian, Christo Wilson, Cong Yu, and Bendert Zevenbergen. Principles for accountable algorithms and a social impact statement for algorithms. Dagstuhl working group write-up. July, 2016. [ PDF | link]
Ifeoma Ajunwa, Sorelle Friedler, Carlos E. Scheidegger, and Suresh Venkatasubramanian. Hiring
by Algorithm: Predicting and Preventing Disparate Impact. Presented at the Yale Law School
Information Society Project conference Unlocking the Black Box: The Promise and Limits of Algorithmic
Accountability in the Professions, Apr. 2, 2016. [PDF]
Philip Adler, Casey Falk, Sorelle A. Friedler, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, Suresh Venkatasubramanian. Auditing Black-box Models by Obscuring Features. arXiv:1602.07043.
[link]
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian.
Certifying and Removing Disparate Impact. Presented at the Fairness, Accountability, and
Transparency in Machine Learning Workshop, Dec. 12, 2014. [link]
F. Betul Atalay, Sorelle A. Friedler, and Dianna Xu. Probabilistic Kinetic Data Structures. Presented
at the Fall Workshop on Computational Geometry, Oct. 25, 2013. [PDF | link]
Sorelle A. Friedler and David M. Mount. Realistic compression of kinetic sensor data. Technical Report CS-TR-4959, University of Maryland, College Park, 2010. [PDF | TR]
Thesis
Sorelle A. Friedler. Geometric Algorithms for Objects in Motion. Dissertation committee: Prof. David Mount (chair), Prof. William Gasarch, Prof. Samir Khuller, Prof. Steven Selden, Prof. Amitabh Varshney. Defense date: July 30, 2010. [PDF] [presentation]
Patents
Mohammed Waleed Kadous, Isaac Richard Taylor, Cedric Dupont, Brian Patrick Williams, Sorelle Alaina Friedler. Permissions based on wireless network data. US 20130244684 A1. Publication date: Sep. 19, 2013.
Sorelle Alaina Friedler, Mohammed Waleed Kadous, Andrew Lookingbill. Position indication controls for device locations. US 20130131973 A1 (also WO 2013078125 A1). Publication date: May 23, 2013.
