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Last Week in AI #33

Fairer AI for healthcare, earthquake prediction, and more!

Last Week in AI #33

Image credit: Rachel Adams / The Conversations

Mini Briefs

A fairer way forward for AI in health care

Machine learning and AI are primed to play an important role in making healthcare more efficient, personalized and effective. However, challenges facing AI in general, like fairness, privacy, access and diversity could have an outsized impact in health care.

For example, data scientists in Chicago, Illinois, found that zip codes were the best predictor of the length of stay for patients in hospitals. On closer inspection, the postal codes were from poor and predominantly African American neighborhoods. Essentially, these issues boil down to data:

“In some health-care systems, there are very basic things that are being ignored, basic quality of care that people are not receiving,” says Kadija Ferryman, an anthropologist at the New York University Tandon School of Engineering who studies the social, cultural and ethical impacts of the use of AI in health care. These inequalities are preserved in the terabytes of health data being generated around the world.

However, the potential benefits of these technologies cannot be ignored. The fact that the people working on these systems are no longer ignorant of the challenges, and the driving motivation of improving health care around the world, is cause for optimisim, with a dose of caution, about AI in health care.

Advances & Business

Concerns & Hype

Analysis & Policy

Expert Opinions & Discussion within the field

  • Google releases data set to help defeat deepfake videos - Google today announced the release of a large corpus of visual deepfakes produced in collaboration with Jigsaw, the Mountain View company’s internal technology incubator. The fight against deepfakes appears to be ramping up.

  • Are You Developing Skills That Won’t Be Automated? - The future of work looks grim for many people. A recent study from Forrester estimated that 10% of U.S. jobs would be automated this year, and another from McKinsey estimates that close to half of all U.S. jobs may be automated in the next decade.

  • We can’t trust AI systems built on deep learning alone - Gary Marcus is not impressed by the hype around deep learning. While the NYU professor believes that the technique has played an important role in advancing AI, he also thinks the field’s current overemphasis on it may well lead to its demise.

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