Expert crowdsourcing with flash teams. - Daniela Retelny; Sébastien Robaszkiewicz; Alexandra To; Walter S. Lasecki; Jay Patel; Negar Rahmati; Tulsee Doshi; Melissa A. Valentine; Michael S. Bernstein (UIST, 2014)

Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations. - Melissa A. Valentine; Daniela Retelny; Alexandra To; Negar Rahmati; Tulsee Doshi; Michael S. Bernstein (CHI, 2017)

Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. - Alex Beutel; Jilin Chen; Tulsee Doshi; Hai Qian; Allison Woodruff; Christine Luu; Pierre Kreitmann; Jonathan Bischof; Ed H. Chi (AIES, 2019)

Fairness in Recommendation Ranking through Pairwise Comparisons. - Alex Beutel; Jilin Chen; Tulsee Doshi; Hai Qian; Li Wei; Yi Wu; Lukasz Heldt; Zhe Zhao 001; Lichan Hong; Ed H. Chi; Cristos Goodrow (KDD, 2019)

Re-imagining Algorithmic Fairness in India and Beyond. - Nithya Sambasivan; Erin Arnesen; Ben Hutchinson; Tulsee Doshi; Vinodkumar Prabhakaran (FAccT, 2021)

LaMDA: Language Models for Dialog Applications. - Romal Thoppilan; Daniel De Freitas; Jamie Hall; Noam Shazeer; Apoorv Kulshreshtha; Heng-Tze Cheng; Alicia Jin; Taylor Bos; Leslie Baker; Yu Du; YaGuang Li; Hongrae Lee; et al. (CoRR, 2022)

Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations. - Flavien Prost; Ben Packer; Jilin Chen; Li Wei; Pierre Kremp; Nick Blumm; Susan Wang; Tulsee Doshi; Tonia Osadebe; Lukasz Heldt; Ed H. Chi; Alex Beutel (CoRR, 2022)