toolings

Spark

World Economic Forum

TOOLKIT

This resource for boards of directors consists of: an introduction; 13 modules intended to align with traditional board committees, working groups and oversight concerns; and a glossary of artificial intelligence (AI) terms.

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

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

SHAP (SHapley Additive exPlanations)

TOOLKIT

A game theoretic approach to explain the output of any machine learning model.

toolings

FairTest: Discovering Unwarranted Associations in Data-Driven Applications

IEEE

FRAMEWORK

IEEE introduces the unwarranted associations (UA) framework, a principled methodology for the discovery of unfair, discriminatory, or offensive user treatment in data-driven applications. The UA framework unifies and rationalizes a number of prior attempts at formalizing algorithmic fairness. It uniquely combines multiple investigative primitives and fairness metrics with broad applicability, granular exploration of unfair treatment in user subgroups, and incorporation of natural notions of utility that may account for observed disparities...

toolings

Ethical OS Toolkit

Institute for the Future

TOOLKIT

The Ethical Operating System can help makers of tech, product managers, engineers, and others get out in front of problems before they happen. It’s been designed to facilitate better product development, faster deployment, and more impactful innovation. All while striving to minimize technical and reputational risks....

toolings

InterpretML

Microsoft

TOOLKIT

Fit interpretable models. Explain blackbox machine learning...

toolings

FairML: Auditing Black-Box Predictive Models

TOOLKIT

We present FairML, an end-to-end toolbox for auditing predictive models by quantifying the relative significance of the model’s inputs. FairML leverages model compression and four input ranking algorithms to quantify a model’s relative predictive dependence on its inputs...