AI Governance

WPF comments to NIST regarding its differential privacy guidance

WPF submitted comments to the National Institute of Standards and Technology regarding its Draft Guidelines for Evaluating Differential Privacy Guarantees. The comments approach the NIST Draft Guidance from a policy perspective, and urged changes to some parts of the definitional language in the Draft Guidance. Key areas of the comments include: A discussion of the

New Report: Risky Analysis: Assessing and Improving AI Governance Tools

We are pleased to announce the publication of a new WPF report, “Risky Analysis: Assessing and Improving AI Governance Tools.” This report sets out a definition of AI governance tools, documents why and how these tools are critically important for trustworthy AI, and where these tools are around the world. The report also documents problems in some AI governance tools themselves, and suggests pathways to improve AI governance tools and create an evaluative environment to measure their effectiveness. AI systems should not be deployed without simultaneously evaluating the potential adverse impacts of such systems and mitigating their risks, and most of the world agrees about the need to take precautions against the threats posed. The specific tools and techniques that exist to evaluate and measure AI systems for their inclusiveness, fairness, explainability, privacy, safety and other trustworthiness issues — called in the report collectively AI governance tools – can improve such issues. While some AI governance tools provide reassurance to the public and to regulators, the tools too often lack meaningful oversight and quality assessments. Incomplete or ineffective AI governance tools can create a false sense of confidence, cause unintended problems, and generally undermine the promise of AI systems. The report contains rich background details, use cases, potential solutions to the problems discussed in the report, and a global index of AI Governance Tools.

Half-day tutorial on AI Governance, Data Protection, and Privacy: Advanced problem-solving for Computer Vision and More

WPF has organized a robust and interactive tutorial on advanced AI governance and privacy for Computer Vision systems (and beyond), to be held at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). WACV is the premier international computer vision event comprised of a main conference and several co-located workshops and tutorials.

What makes this AI governance and data protection tutorial compelling? The 8 speakers for this tutorial are working at the top of their respective fields, with presentations that combine to make a muscular, socio-technical dive into today’s most pressing issues around AI technology, governance, privacy, and policy structures. This tutorial is arranged in a logical flow that moves participants through the technical and the policy aspects of advanced systems development and governance. including technical, legal, ethical, and privacy analysis, as well as emerging norms and additional considerations to be aware of. The tutorial will include ample time for analysis and discussion, and will be participatory.