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Alexander Tolbert

2024  (expected)  Ph.D. Philosophy, University of Pennsylvania
         Dissertation: Causation and Fairness in Machine Learning
                              Advisors: Michael Kearns  and Scott Weinstein  
2022 (expected)  M.A. in Statistics
                             Wharton School of the University of Pennsylvania
2022 (expected)  Graduate Certificates:
                             Advanced Scientific Computing
                             Africana Studies
2019                      M.A. in Philosophy, Virginia Tech
2019                      M.S. in Biochemistry, Virginia Tech
2013                      B.S. in Biology, University of Mobile 

My research focuses on causal inference and its applications to artificial intelligence, machine learning, and fairness. In the last century, computing and automation have evolved from analytical abstractions to become prominent features of the everyday human experience. These advancements in automation have made lives more productive, enjoyable, and informed. However, automated tools are more regularly violating the fundamental rights of individuals and seemingly objective machine learning algorithms often reinforce a racist, or otherwise unjust, status quo. Anonymized datasets are increasingly revealing personally identifiable information, and mathematical models for school admission, as well as risk assessment scores, frequently display racial and gender discrimination. Understanding and improving the science grounding automation that is deeply integrated into our lives is one of the most important methodological and ethical concerns of this generation. Standard attempts to amend automation in high-stakes social contexts have shown to be incomplete and not up to the task. Two criticisms of data science have recently emerged. The first argues that seemingly objective machine learning algorithms often reinforce discrimination. The second chastises practitioners for failing to develop a science of causal inference, rather than a mere collection of techniques for exploiting associations. The first throws into relief an urgent social problem; the second seems like an internal methodological dispute. My research explores how these two critiques are deeply related.

Research Interests

Algorithmic Fairness, Differential Privacy, Causal Inference, Learning Theory, Technology & Law, Justice & Equity
Areas of Specialization  
Philosophy of Science (Causation, Statistics, Machine Learning), Social, Political, & Legal Philosophy, Philosophy of Race
 Areas  of Competence   
Africana Philosophy, History of Science, Ethics