About

Elisa Celis is an assistant professor in the Statistics & Data Science department at Yale University. Her research studies the manifestation of social and economic biases in online lives via the algorithms that encode and perpetuate them. Her work leverages both experimental and theoretical approaches, spanning multiple disciplines including data science, machine learning, fairness in socio-technical systems, and algorithm design. At Yale, she co-founded the Computation and Society Initiative and is a member of the Scholar's Council of UCLA's Center for Critical Internet Inquiry. She is also an affiliated faculty of Yale's Institution for Social and Policy Studies and Cowles Foundation for Research in Economics, and serves on the Advisory Board of the A+ Alliance.

Research topics

• Computer Science
• Artificial Intelligence
• Computer Security
• Machine Learning
• Political Science
• Engineering
• Economics
• Law
• Statistics
• Mathematics

Selected publications

• Designing Closed-Loop Models for Task Allocation

  Frontiers in artificial intelligence and applications · 2023

  • Computer Science
  • Computer Science
  • Machine Learning

  Automatically assigning tasks to people is challenging because human performance can vary across tasks for many reasons. This challenge is further compounded in real-life settings in which no oracle exists to assess the quality of human decisions and task assignments made. Instead, we find ourselves in a “closed” decision-making loop in which the same fallible human decisions we rely on in practice must also be used to guide task allocation. How can imperfect and potentially biased human decisions train an accurate allocation model? Our key insight is to exploit weak prior information on human-task similarity to bootstrap model training. We show that the use of such a weak prior can improve task allocation accuracy, even when human decision-makers are fallible and biased. We present both theoretical analysis and empirical evaluation over synthetic data and a social media toxicity detection task. Results demonstrate the efficacy of our approach.

  DOI

• Towards Just, Fair and Interpretable Methods for Judicial Subset Selection

  2020 · 9 citations

  Senior authorCorresponding
  • Computer Science
  • Political Science
  • Computer Science

  In many judicial systems -- including the United States courts of appeals, the European Court of Justice, the UK Supreme Court and the Supreme Court of Canada -- a subset of judges is selected from the entire judicial body for each case in order to hear the arguments and decide the judgment. Ideally, the subset selected is representative, i.e., the decision of the subset would match what the decision of the entire judicial body would have been had they all weighed in on the case. Further, the process should be fair in that all judges should have similar workloads, and the selection process should not allow for certain judge's opinions to be silenced or amplified via case assignments. Lastly, in order to be practical and trustworthy, the process should also be interpretable, easy to use, and (if algorithmic) computationally efficient. In this paper, we propose an algorithmic method for the judicial subset selection problem that satisfies all of the above criteria. The method satisfies fairness by design, and we prove that it has optimal representativeness asymptotically for a large range of parameters and under noisy information models about judge opinions -- something no existing methods can provably achieve. We then assess the benefits of our approach empirically by counterfactually comparing against the current practice and recent alternative algorithmic approaches using cases from the United States courts of appeals database.

  DOI

Frequent coauthors

• Xavier Alameda-Pineda

  Institut national de recherche en informatique et en automatique

  3 shared

• Deepthi Chander

  3 shared

• Koustuv Dasgupta

  3 shared

• Sakyajit Bhattacharya

  Tata Consultancy Services (India)

  2 shared

• Saraschandra Karanam

  2 shared

• Vaibhav Rajan

  National University of Singapore

  2 shared

• Vijay Keswani

  2 shared

• Мириам Реди

  1 shared

Similar researchers at Yale University

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• Alan Kazdinh-142
• Douglas L. Rothmanh-125