Publications | Emily Black

Emily Black, John Logan Koepke, Pauline Kim, Solon Barocas, Mingwei Hsu (2023). Less Discriminatory Algorithms. Preprint.

Emily Black, Rakshit Naidu, Rayid Ghani, Kit Rodolfa, Daniel Ho, Hoda Heidari (2023). Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools. In EAAMO.

Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, Daniel E Ho (2023). Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features. Preprint.

Emily Black, Klas Leino, Matt Fredrikson (2022). Selective ensembles for consistent predictions. In ICLR.

Emily Black, Zifan Wang, Matt Fredrikson, Anupam Datta (2022). Consistent counterfactuals for deep models. In ICLR.

Emily Black (2022). (Un) Fairness Along the AI Pipeline Problems and Solutions. PhD Thesis.

Emily Black, Manish Raghavan, Solon Barocas (2022). Model multiplicity: Opportunities, concerns, and solutions. In FAccT.

Emily Black, Hadi Elzayn, Alexandra Chouldechova, Jacob Goldin, Daniel Ho (2022). Algorithmic fairness and vertical equity: Income fairness with IRS tax audit models. In FAccT.

Emily Black, Matt Fredrikson (2021). Leave-one-out unfairness. In FAccT.

Dan E Ho, Emily Black, Maneesh Agrawala, Fei-Fei Li (2020). Evaluating facial recognition technology: a protocol for performance assessment in new domains. In Denver Law Review 753.

Emily Black, Joshua Williams, Michael A Madaio, Priya L Donti (2020). A call for universities to develop requirements for community engagement in AI. In CHI.

Emily Black, Samuel Yeom, Matt Fredrikson (2020). Fliptest: fairness testing via optimal transport. In FAccT.

Klas Leino, Emily Black, Matt Fredrikson, Shayak Sen, Anupam Datta (2019). Feature-wise bias amplification. In ICLR.