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Algorithmic Fairness Testing Tools are specialized software applications and frameworks designed to assess, detect, and mitigate biases in artificial intelligence (AI) and machine learning models. These tools help developers and researchers evaluate models against various fairness metrics—such as equalized odds, demographic parity, and individual fairness—to ensure that the models perform equitably across different demographic groups. By providing analytical methods and mitigation strategies, these tools aid in identifying unfair treatment or discriminatory patterns within AI systems. Integrating fairness testing tools into the AI development lifecycle promotes ethical practices, enhances transparency, and supports compliance with legal standards related to discrimination and equality.
There are several tools and frameworks available for testing and mitigating bias in AI models. These tools help developers assess fairness metrics like equalized odds, demographic parity, and individual fairness. The following list is a snapshot of options and does not constitute an endorsement of any one product over another. The intention is to illustrate the various companies and approaches avaialble for achieving algorithmic fairness.
Adversarial Debiasing. A technique that uses adversarial networks to reduce bias in machine learning models. It implements models that learn to make predictions without relying on sensitive attributes and can be incorporated into training pipelines.
Aequitas .An open-source bias and fairness audit toolkit developed by the Center for Data Science and Public Policy at the University of Chicago. Provides a comprehensive set of bias metrics for binary classification models, generates reports to help stakeholders understand bias across different groups, and offers a web interface for interactive analysis.
AI Explainability 360. Also developed by IBM, this toolkit complements AI Fairness 360 by focusing on model interpretability. Provides algorithms to explain data and machine learning models, helping in understanding model decisions, which is crucial for fairness assessments.
Causal Analysis. Tools like Microsoft's DoWhy help in understanding and addressing biases from a causal perspective. Causal analysis provides a framework for identifying the causes of bias and developing strategies to mitigate it.
DataSynthesizer. Primarily a tool for data privacy, DataSynthesizer can generate synthetic datasets that preserve fairness properties. Helps in creating datasets that can be used to test models for fairness and ensures that synthetic data maintain statistical properties of the original data.
DEON. While not a testing tool per se, DEON provides an ethics checklist for data science projects. Includes considerations for fairness and bias, helping teams ensure ethical standards are met throughout the project lifecycle.
Equalized Odds Postprocessing. An algorithm that adjusts the outputs of a classifier to achieve equalized odds. Modifies the prediction labels to balance the error rates across different groups without retraining the model.
EthicalML's Fairness Package. Part of the EthicalML initiative, this package provides resources for fairness in machine learning. Implements various bias mitigation algorithms and offers tools for both pre-processing and post-processing techniques.
Facets. Developed by the Google PAIR team, Facets is a tool for data visualization that can help identify biases in datasets. Provides overviews and detailed views of datasets, helping in detecting imbalances and anomalies in data distributions.
FairCVTest. A tool specifically designed to assess fairness in computer vision models. Evaluates biases in face detection and recognition systems and provides metrics and testing protocols for visual data.
Fairlearn. Developed by Microsoft, Fairlearn is an open-source toolkit that provides algorithms for assessing and improving the fairness of machine learning models. Supports a variety of fairness metrics, including demographic parity and equalized odds. Offers mitigation algorithms like reduction and post-processing techniques, and integrates with popular machine learning frameworks like scikit-learn.
FairML. A Python toolbox auditing black-box predictive models, focusing on understanding the relative significance of inputs. Helps in identifying which features contribute most to model predictions, aiding in the detection of potential biases.
Fairness Indicators. A suite of tools developed by Google to evaluate the fairness of machine learning models. Works well with TensorFlow models but can be adapted for others. Provides metrics like false positive rate and false negative rate across slices of data and integrates with TensorBoard for visualization.
FairSight. FairSight is a visual analytics system for discovering and mitigating bias in classification models. Offers interactive visualizations to explore fairness metrics and supports subgroup analysis to detect hidden biases.
FairTest. A testing toolkit for discovering unintended associations between the outputs of a data-driven system and subgroups defined by protected characteristics. Automates the detection of biases in data and models and provides statistical tools to assess the significance of discovered biases.
Google's What-If Tool. An interactive visual interface that helps users inspect machine learning models without writing code. Allows for exploration of model behavior across different data subsets, supports counterfactual analysis, and helps in understanding fairness metrics.
IBM's AI Fairness 360. An open-source toolkit developed by IBM that provides metrics to check for biases in datasets and machine learning models. Offers a comprehensive set of bias mitigation algorithms, supports both pre-processing and post-processing techniques, and includes tutorials and examples to guide users.
LIME (Local Interpretable Model-Agnostic Explanations). Primarily an interpretability tool, LIME can help assess individual fairness by explaining model predictions. Explains predictions of any classifier in a faithful way by approximating it locally with an interpretable model, helping identify biases affecting individual predictions.
Model Cards. A framework for documenting machine learning models, encouraging transparency and accountability. Provides a structured way to report on the model's performance, intended use, and ethical considerations. Can include fairness metrics and evaluation results.
Prediction Bias Analyzer. An open-source tool that analyzes prediction biases in machine learning models. Provides statistical analysis of model predictions across different groups, helping in identifying systematic errors.
Responsibly. A Python toolkit for auditing and mitigating bias and fairness issues in machine learning models. Offers a variety of bias metrics and visualization tools, supports both classification and regression models, and provides documentation to guide ethical AI practices.
SHAP (SHapley Additive exPlanations). An interpretability tool that provides insight into individual predictions, aiding in assessing fairness. Uses game theory to explain the output of any machine learning model and helps in identifying feature contributions to predictions across different groups.
Themis-ML. A Python library designed to detect and mitigate discrimination in machine learning models. Implements fairness-aware algorithms and bias mitigation techniques, supports metrics like disparate impact and equal opportunity difference, and is compatible with scikit-learn estimators.
VeriFair. A verification tool for fairness properties in machine learning models. Checks models against formal fairness specifications and is useful for models where formal guarantees are required.
WhatIf.py. An open-source tool similar to Google's What-If Tool but available as a Python library. Allows for counterfactual analysis and inspection of model predictions, helping in understanding model behavior across different data subsets.
