We develop practices that enhance trust in data, in AI models, and in the people and processes through which they are deployed.
Projects
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Latest work
Data Provenance Standards. The first cross-industry metadata standards to bring transparency to the origin of datasets used for both traditional data and AI applications.
Enterprise Innovation
Launched 11.30.23
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Coming soon:
GenAI Vendor Assessment Framework.

A cross-industry framework for better assessment of risk and value when procuring third-party Generative AI products.

Technology Innovation
Launched 06.16.25
Framing “Consequential Decisions.”

Harnessing the Promise of AI: Mitigating Potential Harms Through a Risk-Based Approach

Policy
Launched 09.26.24
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Policy Recommendations for the Responsible Use of Artificial Intelligence.

A Policy Roadmap for AI Governance that promotes innovation and competition while prioritizing safety and security.

Policy
Launched 06.13.24
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Responsible Data & AI Diligence for M&A.

A due diligence tool to help M&A teams assess value and risk in acquiring AI startups—from algorithmic discrimination to how culture is an indicator of future value.

Enterprise Innovation
Launched 10.20.22
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Algorithmic Bias Safeguards.

Criteria and education for HR and Procurement teams to evaluate vendors on their ability to detect, mitigate and monitor algorithmic bias in their applications and solutions.

Enterprise Innovation
Launched 12.08.21
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