Alexander Williams Tolbert

Alexander Williams Tolbert

Postdoctoral Researcher, Assistant Professor (Fall 2024), at Emory University’s Department of Quantitative Theory and Methods

Emory University


I am currently a Postdoctoral Researcher at the Department of Quantitative Theory and Methods at Emory University, transitioning to an Assistant Professor role in the same department in the fall of 2024. I am also the co-principal investigator of the AI & Philosophy Lab with Emily Ruth Diana. I completed my Ph.D. in Philosophy at the University of Pennsylvania and concurrently earned a Masters in Statistics from the Wharton School at the same institution.

My doctoral journey was guided by my advisors Anita Allen, a legal scholar and philosopher, Quayshawn Spencer, a philosopher of science and race, and Michael Kearns, a computer scientist and machine learning researcher. My dissertation committee was further enriched with the expertise of Samuel Freeman, a specialist in political philosophy and ethics, Aaron Roth, a computer scientist specializing in machine learning, and Scott Weinstein, a mathematical logician.

Before my time at Penn, I obtained master’s degrees in Biochemistry and Philosophy from Virginia Polytechnic Institute and State University, and a B.S. in Biology from the University of Mobile.

My research is situated at the intersection of philosophy, computation, methodology, and society. I employ a means-end methodology, initiating with the end goal and then working backwards to craft simple, yet potentially insightful models. The central goal when working with a simple model is to devise a solution that unveils a structure, offering insights into the real world. This often involves finding structure through the exploration of various optimization problems.

The objective is not merely to find structures but to unearth those that hold substantial meaning, aiming for a qualitative structure in the solution that could potentially resonate in policy discussions. Essentially, the aspiration is to derive a qualitative structure from a simple model that can be applied to real-world problems, bridging the theoretical and the practical.

Beyond optimization, my approach encompasses the utilization of other mathematical frameworks such as game theory, learning theory, and causal inference to reason about societal issues. This is a continuous endeavor to carve out structured solutions with the potential to influence real policy discussions.

Furthermore, my engagement extends to contemplating issues in political philosophy. This involves not only addressing questions arising from methodology and computation but also delving into broader societal concerns, navigating both ideal and non-ideal theory dimensions in political philosophy.

In addition to my academic pursuits, I have gained industry experience working as a Research Scientist Intern and later as a Research Scientist II Intern at Amazon Web Services, where I engaged in research on algorithmic fairness and bias.

Download my CV .

  • Philosophy of Science
  • Causal Inference
  • Game Theory
  • Machine Learning
  • Social & Political Philosophy
  • Philosophy, Politics & Economics
  • Philosophy of Race
  • PhD in Philosophy, 2023

    University of Pennsylvania

  • MA in Statistics, 2023

    Wharton School, University of Pennsylvania

  • MS in Biochemistry, 2019

    Virginia Tech

  • MA in Philosophy, 2019

    Virginia Tech

  • BS in Biology, 2013

    University of Mobile


Research Scientist II Intern
Amazon Web Services
Jun 2022 – Dec 2022 Remote
Research Scientist Intern
Amazon Web Services
Jun 2021 – Sep 2021 Remote


(2023). Addressing Sensitive Attributes in Algorithmic Decisions through Extended Conditional Independence. American Philosophical Quarterly (Under Review).

(2023). Algorithms, Justice, and The Urban Ghetto. Synthese (forthcoming).

(2023). Causal Agnosticism about Race. Philosophy of Science (forthcoming).


(2023). Causal Knowledge and Fairness in Predictive Data Science. Synthese (Under Review).

(2023). Correcting Underrepresentation and Intersectionality Bias for Fair Classification. Available as a preprint.


(2023). Examining Anti-Ethics Critiques within a Capabilities Model of Reparative Justice. Radical Philosophy Review (Under Review).

(2023). Machine Learning Fairness from Bias Mitigation to Design and Deployment. MIT Press (forthcoming).

(2023). Race and Causation. Philosophy Compass (forthcoming).

(2023). Racial Disregard in AI. American Philosophical Quarterly (Under Review).

(2023). Reconciling Individual Probability Forecasts. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency.