toolings

Tensorflow’s Fairness Evaluation and Visualization Toolkit

Google

TOOLKIT

Fairness Indicators is designed to support teams in evaluating, improving, and comparing models for fairness concerns in partnership with the broader Tensorflow toolkit. The tool is currently actively used internally by many of our products.

toolings

WILDS

Microsoft

DATABASE

WILDS builds on top of recent data collection efforts by domain experts in applications such as tumour identification, wildlife monitoring and poverty mapping, presenting a unified collection of datasets with evaluation metrics and train/test splits that the researchers believe are representative of real-world distribution shifts.

toolings

Themis Fairness Testing: Testing Software for Discrimination

RESEARCH

This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior...

toolings

Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigation

LIBRARY

themis-ml is a Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms...

toolings

StereoSet

MIT

DATASET

StereoSet is a dataset that measures stereotype bias in language models. StereoSet consists of 17,000 sentences that measures model preferences across gender, race, religion, and profession.

toolings

The LinkedIn Fairness Toolkit (LiFT)

Linkedin

LIBRARY

The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows. The library can be deployed in training and scoring workflows to measure biases in training data, evaluate fairness metrics for ML models, and detect statistically significant differences in their performance across different subgroups. It can also be used for ad-hoc fairness analysis.

toolings

MinDiff

Google

FRAMEWORK

Google today released MinDiff, a new framework for mitigating (but not eliminating) unfair biases when training AI and machine learning models. The company says MinDiff is the culmination of years of work and has already been incorporated into various Google products, including models that moderate content quality.

toolings

Remove problematic gender bias from word embeddings

RESEARCH

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases...

toolings

Debiasing Representations by Removing Unwanted Variation Due to Protected Attributes

RESEARCH

We propose a regression-based approach to removing implicit biases in representations. On tasks where the protected attribute is observed, the method is statistically more efficient than known approaches. Further, we show that this approach leads to debiased representations that satisfy a first order approximation of conditional parity. Finally, we demonstrate the efficacy of the proposed approach by reducing racial bias in recidivism risk scores.