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Jennifer King | Stanford HAI

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peoplePolicy Fellow

Jennifer King

Privacy and Data Policy Fellow, Stanford HAI

Jennifer King

Dr. Jennifer King is the Privacy and Data Policy Fellow at the Stanford University Institute for Human-Centered Artificial Intelligence. An information scientist by training, Dr. King is a recognized expert and scholar in information privacy. Sitting at the intersection of human-computer interaction, law, and the social sciences, her research examines the public’s understanding and expectations of online privacy as well as the policy implications of emerging technologies. Most recently, her research explored alternatives to notice and consent (with the World Economic Forum), the impact of California’s new privacy laws, and dark patterns. Her past work includes projects focusing on social media, genetic privacy, mobile application platforms, the Internet of Things (IoT), and digital surveillance. Her scholarship has been recognized for its impact on policymaking by the Future of Privacy Forum, and she has been an invited speaker before the Federal Trade Commission at several Commission workshops. She has been featured in numerous publications and outlets, including The New York Times, the Washington Post, the Los Angeles Times, Wired, Recode, National Public Radio, CNBC, Bloomberg, CNET, Vox, Consumer Reports, NBC News, MIT Technology Review, among others.

Dr. King completed her doctorate in Information Management and Systems at the University of California, Berkeley School of Information. Prior to joining HAI, Dr. King was the Director of Consumer Privacy at the Center for Internet and Society at Stanford Law school from 2018 to 2020. Before coming to Stanford, she was a co-director of the Center for Technology, Society, and Policy, a graduate student led research center at UC Berkeley, and was a privacy researcher at the Samuelson Law, Technology, and Public Policy Clinic at Berkeley Law. She was a member of the California State Advisory Board on Mobile Privacy Policies and the California State RFID Advisory Board. She received her Master’s in Information Management and Systems also from the University of California, Berkeley’s School of Information, and her undergraduate degree in Political Science and Sociology from the University of California, Irvine. Prior to entering academia she worked in security and in product management for several Internet companies, most notably Yahoo!.

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Latest Related to Jennifer King

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23andMe’s DNA Database Is Up For Sale. Who Might Want It, And What For?

Washington Post
Privacy, Safety, SecurityIndustry, InnovationEthics, Equity, InclusionMar 25

After 23andMe announced that it’s headed to bankruptcy court, it’s unclear what happens to the mass of sensitive genetic data that it holds. Jen King, Policy Fellow at HAI comments on where this data could end up and be used for.

response to request

Response to OSTP’s Request for Information on the Development of an AI Action Plan

Rishi Bommasani, Daniel E. Ho, Percy Liang, Jennifer King, Russell Wald, Caroline Meinhardt, Daniel Zhang
Regulation, Policy, GovernanceMar 17

In this response to a request for information issued by the National Science Foundation’s Networking and Information Technology Research and Development National Coordination Office (on behalf of the Office of Science and Technology Policy), scholars from Stanford HAI, CRFM, and RegLab urge policymakers to prioritize four areas of policy action in their AI Action Plan: 1) Promote open innovation as a strategic advantage for U.S. competitiveness; 2) Maintain U.S. AI leadership by promoting scientific innovation; 3) Craft evidence-based AI policy that protects Americans without stifling innovation; 4) Empower government leaders with resources and technical expertise to ensure a “whole-of-government” approach to AI governance.

whitepaper

Rethinking Privacy in the AI Era: Policy Provocations for a Data-Centric World

Jennifer King, Caroline Meinhardt
Privacy, Safety, SecurityRegulation, Policy, GovernanceFeb 22

This white paper explores the current and future impact of privacy and data protection legislation on AI development and provides recommendations for mitigating privacy harms in an AI era.

All Related

The Privacy-Bias Trade-Off
Arushi Gupta, Helen Webley-Brown, Victor Y. Wu, Daniel E. Ho, Jennifer King
Oct 19, 2023
policy brief

Algorithmic fairness and privacy issues are increasingly drawing both policymakers’ and the public’s attention amid rapid advances in artificial intelligence (AI). But safeguarding privacy and addressing algorithmic bias can pose a less recognized trade-off. Data minimization, while beneficial for privacy, has simultaneously made it legally, technically, and bureaucratically difficult to acquire demographic information necessary to conduct equity assessments. In this brief, we document this tension by examining the U.S. government’s recent efforts to introduce government-wide equity assessments of federal programs. We propose a range of policy solutions that would enable agencies to navigate the privacy-bias trade-off.

The Privacy-Bias Trade-Off

Arushi Gupta, Helen Webley-Brown, Victor Y. Wu, Daniel E. Ho, Jennifer King
Oct 19, 2023

Algorithmic fairness and privacy issues are increasingly drawing both policymakers’ and the public’s attention amid rapid advances in artificial intelligence (AI). But safeguarding privacy and addressing algorithmic bias can pose a less recognized trade-off. Data minimization, while beneficial for privacy, has simultaneously made it legally, technically, and bureaucratically difficult to acquire demographic information necessary to conduct equity assessments. In this brief, we document this tension by examining the U.S. government’s recent efforts to introduce government-wide equity assessments of federal programs. We propose a range of policy solutions that would enable agencies to navigate the privacy-bias trade-off.

Privacy, Safety, Security
policy brief
Responses to NTIA's Request for Comment on AI Accountability Policy
Arvind Narayanan, Sayash Kapoor, Rishi Bommasani, Percy Liang, Jennifer King, Daniel Zhang
Jun 14, 2023
response to request
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Responses to NTIA's Request for Comment on AI Accountability Policy

Responses to NTIA's Request for Comment on AI Accountability Policy

Arvind Narayanan, Sayash Kapoor, Rishi Bommasani, Percy Liang, Jennifer King, Daniel Zhang
Jun 14, 2023

Responses to NTIA's Request for Comment on AI Accountability Policy

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response to request
Response to FTC's Advanced Notice of Proposed Rulemaking on Commercial Surveillance and Data Security
Abel Ribbink, Pete Warden, James Zou, Jennifer King, Caroline Meinhardt, Daniel Zhang
Nov 21, 2022
response to request
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Response to FTC's Advanced Notice of Proposed Rulemaking on Commercial Surveillance and Data Security

Response to FTC's Advanced Notice of Proposed Rulemaking on Commercial Surveillance and Data Security

Abel Ribbink, Pete Warden, James Zou, Jennifer King, Caroline Meinhardt, Daniel Zhang
Nov 21, 2022

Response to FTC's Advanced Notice of Proposed Rulemaking on Commercial Surveillance and Data Security

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response to request
Recommendations on Updating the National Artificial Intelligence Research and Development Strategic Plan
Daniel E. Ho, Jennifer King, Russell Wald, Daniel Zhang
Mar 09, 2022
response to request

Stanford HAI submitted this response in March 2022 to support the work of the White House Office of Science and Technology Policy to update the National Artificial Intelligence Research and Development Strategic Plan.

Recommendations on Updating the National Artificial Intelligence Research and Development Strategic Plan

Daniel E. Ho, Jennifer King, Russell Wald, Daniel Zhang
Mar 09, 2022

Stanford HAI submitted this response in March 2022 to support the work of the White House Office of Science and Technology Policy to update the National Artificial Intelligence Research and Development Strategic Plan.

response to request
Advancing the Case for Data Intermediaries
Jennifer King
PhD
Feb 16, 2022
news

A new report from the World Economic Forum calls for third-party data protection. This approach would respect individual rights and benefit companies and society.

Advancing the Case for Data Intermediaries

Jennifer King
PhD
Feb 16, 2022

A new report from the World Economic Forum calls for third-party data protection. This approach would respect individual rights and benefit companies and society.

news
Building a National AI Research Resource
Christopher Wan, Daniel E. Ho, Jennifer King, Russell Wald
Oct 01, 2021
whitepaper
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Stanford HAI co-directors were among the first to issue a call for the U.S. government to create a National AI Research Resource.

Building a National AI Research Resource

Christopher Wan, Daniel E. Ho, Jennifer King, Russell Wald
Oct 01, 2021

Stanford HAI co-directors were among the first to issue a call for the U.S. government to create a National AI Research Resource.

Regulation, Policy, Governance
Privacy, Safety, Security
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whitepaper
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