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

A deep exploration of Algorithmic Empathy, the next frontier in AI understanding. This project examines how machines can learn from human fallibility, model disagreement, and align with moral reasoning. It blends psychology, fairness metrics, interpretability, and co-learning design into one framework for humane intelligence.

AI systems are making decisions about your freedom, your job, and your healthcare. This project shows the bias is real and how to fix it.

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.

Detecting bias in ML models using heat maps

Social and Ethical Issues in Information Technology - material and project

Teaching material for bachelor course at Arcada

Dataset, images, metadata, and analysis scripts used in a study on representational bias in AI-generated imagery for university visual communication.

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

Analyzing geographic and cultural bias in AI therapy advice. Interactive visualization showing how AI systems draw from predominantly Anglophone sources when advising users about culturally specific dilemmas in India, Nigeria, and the Philippines.

Anexo técnico de la tesis "Gobernanza de la IA en la Administración Tributaria en México"

AI governance audit system for healthcare decision models. Evaluates fairness (DPD/EOD), calibration, and robustness with NIST AI RMF-aligned reporting.

AI flub ups

A comparative ethical audit of commercial Computer Vision APIs (Amazon Rekognition, Face++, and Google Cloud Vision) against 19 best practices for Automated Gender Recognition (AGR).