bias-in-credit-models

WEFE: The Word Embeddings Fairness Evaluation Framework. WEFE is a framework that standardizes the bias measurement and mitigation in Word Embeddings models. Please feel welcome to open an issue in case you have any questions or a pull request if you want to contribute to the project!

Climate science package for Julia

Solutions on Practical Data Science Specialization on Coursera (offered by deeplearning.ai)

Official repository for the paper "ALERT: A Comprehensive Benchmark for Assessing Large Language Models’ Safety through Red Teaming"

Multi-Calibration & Multi-Accuracy Boosting for R

Official code of "Discover and Mitigate Unknown Biases with Debiasing Alternate Networks" (ECCV 2022)

Code and data for Koo et al's ACL 2024 paper "Benchmarking Cognitive Biases in Large Language Models as Evaluators"

Official code of "Discover the Unknown Biased Attribute of an Image Classifier" (ICCV 2021)

Master thesis: Exploring bias in German NLG (GPT-3 & GerPT-2). Applies regard classification and bias mitigation triggers.

A survey of fairness in contextualized language models