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Ricardo Silva on Seminar Series: Causal Inference and Data Science for Improved Policy

On March 8, 2024, experts and enthusiasts in causal inference and data science gathered at the Data Sciences Institute for a seminar titled “Some Thoughts on the Use of Causal Modelling in Algorithmic Fairness,” presented by Professor Ricardo Silva from University College London (UCL). Funded through the DSI Emergent Data Sciences Program competition, and co-led by Professors Linbo Wang (Departments of Statistical Sciences and Computer Science), Gustavo J. BobonisRaji Jayaraman, and Ismael Mourifié (Department of Economics) at the University of Toronto, this event marks the inaugural talk of a speaker series bridging the gap between causal inference and policy.


Ricardo Silva, Professor of Statistical Machine Learning and Data Science, Department of Statistical Science, UCL




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Data-driven and automated decision-making can scale services and standardize policies, but often fails to ensure fairness due to biases in datasets, disadvantaging certain demographic groups. Ricardo Silva explores how causal reasoning and inference can address algorithmic fairness by examining hypothetical scenarios and policies that could change the balance of outcomes across demographic groups.