Kate Crawford

Kate Crawford is a leading researcher, academic and author who has spent the last decade studying the social implications of data systems, machine learning and artificial intelligence. She is a Distinguished Research Professor at New York University, a Principal Researcher at Microsoft Research New York, and a Visiting Professor at the MIT Media Lab. Her recent publications address data bias and fairness, social impacts of artificial intelligence, predictive analytics and due process, and algorithmic accountability and transparency.

Kate is the co-founder and co-director of the AI Now Research Institute, along with Meredith Whittaker: a new interdisciplinary research center dedicated to studying the social impacts of artificial intelligence. In July 2016, she co-chaired the Obama White House symposium on the impacts of AI in the near term. The symposium addressed artificial intelligence across four domains: labor, health, social inequality and ethics.

Her academic research has been published in highly ranked journals such as Nature, New Media & Society, Science, Technology & Human Values and Information, Communication & Society. Apart from the academic stuff, Kate has also written for The New York Times, The Atlantic, Harpers’ Magazine, and New Inquiry, among others.

You can email ea [at] ainowinstitute [dot] org. For press, please contact press [at] ainowinstitute [dot] org. Or try @katecrawford on Twitter. If you're so inclined, here's a PGP Key.

Anatomy of AI

What does it really take to make Alexa tell you the weather? This collaborative essay and exploded diagram of a single Amazon Echo shows the deep networks required to build AI at scale.

Royal Society Lecture 2018

Kate spoke at the Royal Society as part of their prestigious series featuring leaders in AI. Watch her talk here: "Just an Engineer: The Politics of AI"

Wired interview

Why AI is still waiting for its ethics transplant, interview with Scott Rosenberg

NIPS Keynote: 'The Trouble with Bias'

Kate spoke to 7,000 people at NIPS about machine learning and bias, the differences between representational and allocative bias, and the deeper issues with classification.