Publications

2020


Geiger, R. Stuart, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, and Jenny Huang. 2020. “Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?” In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAT* ’20), January 27–30, 2020, Barcelona, Spain. ACM, New York, NY, USA.

https://stuartgeiger.com/papers/gigo-fat2020.pdf

https://doi.org/10.1145/3351095.3372862


2019

 

Burrell, J. Z. Kahn, A. Jonas, and D. Griffin (2019) When Users Control the Algorithms: values expressed in practices on Twitter. Proceedings of CSCW.


Mulligan, Deirdre K., Joshua A. Kroll, Nitin Kohli, and Richmond Y. Wong. (2019) “This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology.” Proceedings of CSCW.

Andrus, McKane and Thomas Krendl Gilbert (2019) Towards a Just Theory of Measurement: A Principled Social Measurement Assurance Program for Machine Learning. AI Ethics and Society (AIES).

Mulligan, Deirdre K. and Kluttz, Daniel and Kohli, Nitin. (2019). Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions (July 7, 2019). Available at SSRN: https://ssrn.com/abstract=3311894 or http://dx.doi.org/10.2139/ssrn.3311894

 

Kluttz, Daniel and Deirdre K. Mulligan (2019) Automated Decision Support Technologies and the Legal Profession. Berkeley Technology Law Journal (forthcoming)

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3443063

 

 

2018

 

Mulligan, D and D. Griffin (2018) “Rescripting Search To Respect the Right to Truth” in

Georgetown Law Tech Review, 557. (https://bit.ly/2KXt5ga)

 

Roel Dobbe, Sarah Dean, Thomas Gilbert, and Nitin Kohli (2018) A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics.
poster presented at FAT/ML 2018.