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HAI In the News

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June 25, 2021

For Jennifer King, HAI Privacy and Data Policy Fellow, the notice-and-consent paradigm as it currently stands is a “farce” that needs an overhaul — not just from a legal standpoint, but also from a human-technology interaction perspective.

June 17, 2021

The biggest-ever study of real people's mortgage data, led by HAI Faculty Affiliate Laura Blattner, shows that predictive tools to approve or reject loans are less accurate for minorities.

May 24, 2021

On this NPR affiliate station, HAI Associate Director Daniel Ho joins FORUM to talk about important considerations for ethical use of facial recognition technology.

May 20, 2021

HAI Associate Director Rob Reich says that leaving critical questions to AI scientists who answer to corporate leaders is a trend we must reverse. The frontier of AI science should be in universities.

May 20, 2021

HAI Senior Fellow and Digital Economy Lab Director Erik Brynjolfsson writes that when it comes to AI’s effect on the workforce, the real challenge is wages, not jobs.

May 17, 2021

HAI Co-Directors John Etchemendy and Fei-Fei Li advocate for access to data and greater computational power in the form of a National Research Cloud

May 17, 2021

HAI Faculty Affiliate Christopher Potts says AI agents don't have human-level abilities yet, but questions of responsibility, rights, and moral status need to be discussed today

April 30, 2021

HAI Co-Director Fei-Fei Li and Andrew Ng, Stanford Professor and Founder of DeepLearning, share their thoughts in a co-hosted event on Healthcare's AI Future

April 28, 2021

HAI Privacy and Data Policy Fellow Jennifer King illuminates the data-sharing practices behind ad tracking

April 22, 2021

The annual AI Index is packed full of data and graphs; this article highlights 15 that provide a snapshot of the current state of AI

April 13, 2021

HAI Faculty Affiliate James Zou shares his research on how regulatory reviews of medical devices could be improved

April 5, 2021

HAI Associate Director Daniel Ho discusses the importance of adjusting inferences to account for potential bias