Symposium on Predictive Analytics

WPF’s Pam Dixon is a presenter at the Ohio State University Moritz College of Law Symposium on Predictive Analytics March 24. WPF’s role in the event is to discuss consumer scoring and The Scoring of America report, and discussion of Dixon’s recent research in AI and policy.

The panel WPF is joining is focused on different sectoral aspects of predictive analytics:

Panel: Predictive Analytics: Challenging Issues in Specific Private Sector Contexts
Pam Dixon, Founder and Executive Director, World Privacy Forum
Nathan Cortez, Associate Dean for Research, Gerald J. Ford Research Fellow, and the Adelfa Botello Callejo Endowed Professor of Law in Leadership and Latino Studies
Matt Bodie, Callis Family Professor of Law and Director, Master of Science in Human Resources Law Program, Center for Employment Law, St. Louis University School of Law

The full roster of participants and additional conference information is available at: http://moritzlaw.osu.edu/students/groups/is/symposia/predictive-analytics-law-and-policy-mapping-the-terrain/

This fall, the I/S journal will be publishing articles from symposium participants.

Posted March 25, 2017 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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