Trusted AI - LF AI

Trust and responsibility should be core principles of AI. We encourage you to infuse these guiding principles and technologies for trust and transparency into your AI project.

The LF AI & Data Trusted AI Committee is a global group working on policies, guidelines, tools, and use cases by industry to ensure the development of trustworthy AI systems and processes to develop them continue to improve over time. Future directions include creating a badging or certification process for open source projects that meet the Trusted AI policies/guidelines defined by LF AI & Data. Trusted AI principles defined by the Principles Working Group are defined below.

To view the Trusted AI projects on the LF AI & Data landscape, please click here.

Projects

AI Fairness 360

AI Fairness 360, an LF AI incubation project, is an extensible open source toolkit that can help users examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle.

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AI Explainability 360

The AI Explainability 360 toolkit, an LF AI Foundation incubation project, is an open-source library that supports the interpretability and explainability of datasets and machine learning models.

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Adversarial Robustness Toolkit

Adversarial Robustness Toolbox (ART), an LF AI Foundation incubation project, provides tools that enable developers and researchers to evaluate, defend, certify and verify Machine Learning models and applications against the adversarial threats.

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Principles for Trusted AI

The (R)REPEATS acronym captures the Trusted AI Principles of Reproducibility, Robustness, Equitability, Privacy, Explainability, Accountability, Transparency, and Security. The principles in the (R)REPEATS image are presented in no particular order other than to match the acronym, as they are of equal importance. To find out more about the Principles for Trusted AI please check the blog LF AI & Data Announces Principles for Trusted AI.