toolings
BIAS LANGUAGE MODEL

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
BIAS FAIRNESS

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
BIAS FAIRNESS

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
BIAS ETHICS & TRUST Uncategorised

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
BIAS FAIRNESS

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
BIAS ETHICS & TRUST Uncategorised

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.

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