All 3rd Party tools for Responsible AI

Responsible AI; RAI is umbrella term for a trustworthy AI models. It includes-

  1. Fairness of Data and model ( gender, racial biases)

  2. Explainability of Data and model ( local and global interpretability and explainability of models)

  3. Robustness ( Security and safety of model and data)

  4. Soundness ( Data Quality and model performance)

  5. Accountability ( Governance and compliance)

  6. Privacy ( Data Ethics and human consent)

  7. Sustainability( Societal and Environment well- being)

There are multiple frameworks available to test above mentioned components of RAI. Here we list few-




VENDOR

framework/package name

RAI Component

IBM

Watson Openscale

Fairness and Transparency :Analyze the asset/solution with trust and transparency and understand how the model makes decision. Automate AI at scale with transparent, explainable outcomes that are free from harmful bias and drift

​IBM

AI Fairness 360

Fairness: Examine, report, and mitigate discrimination and bias in ML models

IBM

AI Explainability 361

Explainability and Interpretability

IBM

Diffprivlib

Privacy: explore the impact of differential privacy on ML accuracy using classification and clustering models

Google

What-If Tool

Explainability and Interpretability: using visualization your dataset automatically

Google

Explainable AI Tool

Explainability : Design and build interpretable AI

Google

Differential Privacy Library

Privacy

Microsoft

InterpretML

Transparency

Microsoft

Fairlearn

Fairness

Microsoft & OpenDP

SmartNoise

Privacy

Amazon

Sagemaker Clarify

Transparency

DataRobot

Bias & Fairness Testing

Fairness

H20 Driverless AI

Transparency

Diveplane

Understandable AI

Transparency

Diveplane

GEMINAI

Privacy

Facebook & Pytorch

Opacus

Privacy

DataRobot

Explainable AI

Transparency

LinkedIn

LiFT

Fairness

Fairkit-Learn

Fairkit-Learn

Fairness

Aequitas

Bias & Fairness Audit

Fairness




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