There are 5 repositories under bias-detection topic.
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Curated list of open source tooling for data-centric AI on unstructured data.
Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
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!
To help public defenders better serve their clients, Open Sentencing shows racial bias in data such as demographics providing insights for each case
Solutions on Practical Data Science Specialization on Coursera (offered by deeplearning.ai)
HonestyMeter: An NLP-powered framework for evaluating objectivity and bias in media content, detecting manipulative techniques, and providing actionable feedback.
Tools for diagnostics and assessment of (machine learning) models
"Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color" (ICCV 2023)
Detection of propaganda or partisan allegiance in natural text.
A curated list of Robust Machine Learning papers/articles and recent advancements.
Official code of "Discover and Mitigate Unknown Biases with Debiasing Alternate Networks" (ECCV 2022)
Code & Data for the paper "RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models"
Examples of unfairness detection for a classification-based credit model
Official code of "Discover the Unknown Biased Attribute of an Image Classifier" (ICCV 2021)
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. Paper presented at MICCAI 2023 conference.
This repository contains a console-interface name-ethnicity classifier
Master thesis: Exploring bias in German NLG (GPT-3 & GerPT-2). Applies regard classification and bias mitigation triggers.
Code and data for Koo et al's ACL 2024 paper "Benchmarking Cognitive Biases in Large Language Models as Evaluators"
A curated list of Distribution Shift papers/articles and recent advancements.
Materials to reproduce findings in our story, "Google Ad Portal Equated 'Black Girls' With Porn"
A survey of fairness in contextualized language models
Our submission to the SemEval2019 shared task on Hyperpartisan News Detection.
A program to automate testing open source LLMs for their political compass scores
Our ML model calculates the biasness of a political article based on linguistic features and classifies them as biased towards the ruling government, bias towards the opposition, or neutral.
Author Bias Computation and Scientometric Plotting
Find here the analysis of the data for the experiment when an unconscious preference is happening in real time
Language has a profound impact on our thoughts, perceptions, and conceptions of gender roles. Gender-inclusive language is, therefore, a key tool to promote social inclusion and contribute to achieving gender equality. Consequently, detecting and mitigating gender bias in texts is instrumental in halting its propagation and societal implications. However, there is a lack of gender bias datasets and lexicons for automating the detection of gender bias using supervised and unsupervised machine learning (ML) and natural language processing (NLP) techniques. Therefore, the main contribution of this work is to publicly provide labeled datasets and exhaustive lexicons by collecting, annotating, and augmenting relevant sentences to facilitate the detection of gender bias in English text. Towards this end, we present an updated version of our previously proposed taxonomy by re-formalizing its structure, adding a new bias type, and mapping each bias subtype to an appropriate detection methodology. The released datasets and lexicons span multiple bias subtypes including: Generic He, Generic She, Explicit Marking of Sex, and Gendered Neologisms. We leveraged the use of word embedding models to further augment the collected lexicons. The underlying motivation of our work is to enable the technical community to combat gender bias in text and halt its propagation using ML and NLP techniques.
Bert Classification on Jigsaw Data with Gender as a basic genre, followed by identifying Bias in Toxic Classification.
Ever wondered if you could identify the media outlet which published an article based on text alone? Fiat Lux will answer these questions and more!