Lab Materials for MIT 6.S191: Introduction to Deep Learning

A reading list and fortnightly discussion group designed to provoke discussion about ethical applications of, and processes for, data science.

Computational Social Science Project: "Algorithmic Bias in Echo Chamber Formation".

[MLHC 2020] Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts (Jabbour, Fouhey, Kazerooni, Sjoding, Wiens). https://arxiv.org/abs/2009.10132

Analyzing clinical decision instruments through the lens of data and large language models.

FairBook: A Reproducibility Study on The Unfairness of Popularity Bias in Book Recommendation (Bias@ECIR 2022)

Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions on Fairness, Accuracy and Population groups

Workshop with readings and exercises on the politics of tech.

Teaching material for bachelor course at Arcada

Detecting bias in ML models using heat maps

Social and Ethical Issues in Information Technology - material and project

AI flub ups

Demonstrates the use of bias mitigation algorithms from IBM's AIF360 toolkit.

Automated tool to evaluate Twitter saliency filter algorithmic bias

Exercise repository for Algorithmic Fairness, Accountability and Ethics (Spring 2022), IT University of Copenhagen

Affevtive Bias in Large Pre-trained Language Models

Fairness in Digital Image Forgery Detection System