A multi-disciplinary group reading papers on the topic of fairness and ethics in Machine Learning and Data Science.
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
"This Whole Thing Smacks of Gender": Algorithmic Exclusion in Bioimpedance-based Body Composition Analysis
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
PROBAST in use
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
A Unified Approach to Interpreting Model Predictions (SHAP paper) https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities
The Myth of Complete AI-Fairness
Fair Bayesian Optimization
Ethical considerations in multilabel text classifications
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
Artificial Intelligence and the Purpose of Social Systems