Princeton’s Center for Information Technology Policy (CITP) recently launched an initiative on Artificial Intelligence, Machine Learning, and Public Policy. On Friday, December 8, 2017, we’ll be in Washington DC talking about AI and policy. The event is at the National Press Club, at 12:15-2:15pm on Friday, December 8. Lunch will be provided for those who […]
AI Mental Health Care Risks, Benefits, and Oversight: Adam Miner at Princeton
October 18, 2017 by
How does AI apply to mental health, and why should we care? Today the Princeton Center for IT Policy hosted a talk by Adam Miner, ann AI psychologist, whose research addresses policy issues in the use, design, and regulation of conversational AI in health. Dr. Miner is an instructor in Stanford’s Department of Psychiatry and […]
Getting serious about research ethics: AI and machine learning
September 18, 2017 by
[This blog post is a continuation of our series about research ethics in computer science.] The widespread deployment of artificial intelligence and specifically machine learning algorithms causes concern for some fundamental values in society, such as employment, privacy, and discrimination. While these algorithms promise to optimize social and economic processes, research in this area has […]
Multiple Intelligences, and Superintelligence
May 4, 2017 by
Superintelligent machines have long been a trope in science fiction. Recent advances in AI have made them a topic for nonfiction debate, and even planning. And that makes sense. Although the Singularity is not imminent–you can go ahead and buy that economy-size container of yogurt–it seems to me almost certain that machine intelligence will surpass ours eventually, and quite […]
Language necessarily contains human biases, and so will machines trained on language corpora
August 24, 2016 by
I have a new draft paper with Aylin Caliskan-Islam and Joanna Bryson titled Semantics derived automatically from language corpora necessarily contain human biases. We show empirically that natural language necessarily contains human biases, and the paradigm of training machine learning on language corpora means that AI will inevitably imbibe these biases as well. Specifically, we look at […]
