WPF participating in NYU’s KAVLI ethics workgroup, co-moderating

WPF Executive Director Pam Dixon will be co-moderating a portion of New York University’s KAVLI Human project ethics working group meeting to discuss research guidelines for the project. KAVLI is a wide-ranging, significant longitudinal human subjects research study that also crosses into new areas of big data research. WPF is pleased to be able to participate meaningfully in the foundational ethics issues in this groundbreaking project.

Meeting location: NYU

Date: Friday, Sept. 23, 2016

The meeting is by invitation only, however, more information about the KAVLI research project is available here: http://kavlihumanproject.org.

More background on Kavli from kavlihumanproject.org:

“The Kavli HUMAN Project will be the first true study of all of the factors that make humans… human. For the first time ever we are now able to quantify the human condition using rigorous science and big data approaches to understand what makes us well and what makes us ill by measuring the feedback mechanisms between biology, behavior, and our environment in the bio-behavioral complex.”

Posted September 21, 2016 in

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.
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