Shira Gur Arieh

Shira Gur-Arieh

S.J.D. Candidate

Graduate Student Fellow, Berkman Klein Center
LL.M. Advisor

sgurarieh at sjd.law.harvard.edu

Dissertation

Essays On Legitimacy in Machine-Learning Algorithms

My research examines questions that relate to legitimacy in machine-learning algorithms, and addresses whether they meet minimal conditions to deserve compliance from their subjects. Many of these questions originate in the ways in which algorithmic prediction meaningfully departs from traditional, human decision-making processes. Predictive algorithms detect patterns from historical data and apply them to individuals, with the narrow goal of maximizing predictive accuracy. Humans rarely maximize accuracy at all costs, but rather incorporate a range of values when making decisions, such as merit, desert, agency and equality. While humans typically pay attention to the distinctive aspects of each individual case, a predictive algorithm will infer an individual’s future behavior from the historical behavior of his statistical peers. Humans tend to ground their decisions in logical and intelligible explanations, whereas algorithmic predictions are driven by statistical correlations, which may lack intuitive support. For these reasons and others, good predictions do not necessarily lead to good decisions, or to otherwise just and desirable outcomes. This gap between machine predictions and human decisions is not always challenged by existing notions of fairness. In my dissertation, I shed light on structural features of machine-learning algorithms, and analyze how they may undermine the fundamental legitimacy of their use.

Fields of Research and Supervisors

  • Legitimacy in socio-legal studies, with applications to new machine-learning and large language model technologies, with Professor Martha Minow, Harvard Law School, Principal Faculty Supervisor
  • Anti-discrimination law, and normative and legal theories of fairness with Professor Benjamin Eidelson, Harvard Law School
  • Theoretical foundations of algorithms, algorithmic design, and fair machine-learning with Professor Sharad Goel, Harvard Kennedy School

Additional Research Interests

  • Empirical Legal Studies
  • Behavioral Economics
  • Law and Public Policy
  • Moral Philosophy

Education

  • Harvard Law School, S.J.D. Candidate 2023 – Present
  • Harvard Law School, LL.M. 2023 (requirements fulfilled, degree waived)
  • Hebrew University of Jerusalem, LL.B. 2020

Academic Appointments and Fellowships

  • Berkman Klein Center for Internet & Society, Graduate Student Fellow, 2023–2024
  • Harvard Law School, 2023-2024, Graduate Program Fellow, LL.M. Advisor

Representative Publications

Shira Gur-Arieh & Oren Gazal-Ayal, Supervised Release as a Mechanism to Decrease Perceived    Dangerousness of Defendants (forthcoming, Mishpatim L. Rev). available at: https://lawjournal.huji.ac.il/article/12/1898) [Hebrew].

Additional Information

Languages: English, Hebrew

Last Updated: August 18, 2023