A reading list and fortnightly discussion group designed to provoke discussion about ethical applications of, and processes for, data science.
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Updated
May 21, 2024 - Python
A reading list and fortnightly discussion group designed to provoke discussion about ethical applications of, and processes for, data science.
Lab Materials for MIT 6.S191: Introduction to Deep Learning
Fairness in Digital Image Forgery Detection System
Analyzing clinical decision instruments through the lens of data and large language models.
AI flub ups
Computational Social Science Project: "Algorithmic Bias in Echo Chamber Formation".
Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions on Fairness, Accuracy and Population groups
Affevtive Bias in Large Pre-trained Language Models
Demonstrates the use of bias mitigation algorithms from IBM's AIF360 toolkit.
[MLHC 2020] Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts (Jabbour, Fouhey, Kazerooni, Sjoding, Wiens). https://arxiv.org/abs/2009.10132
Exercise repository for Algorithmic Fairness, Accountability and Ethics (Spring 2022), IT University of Copenhagen
FairBook: A Reproducibility Study on The Unfairness of Popularity Bias in Book Recommendation (Bias@ECIR 2022)
Detecting bias in ML models using heat maps
Automated tool to evaluate Twitter saliency filter algorithmic bias
Workshop with readings and exercises on the politics of tech.
Social and Ethical Issues in Information Technology - material and project
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