Testing AI fairness in predicting college dropout rate cornell.edu - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from cornell.edu Daily Mail and Mail on Sunday newspapers.
Artificial intelligence has huge potential for many healthcare challenges – but there are still many hurdles that must be overcome.
At a keynote for the Association for Computing Machinery Conference on Health, Inference and Learning this past week, Dr. Alan Karthikesalingam, research lead at Google Health UK, described three myths commonly encountered in the path to building and translating AI models in clinical settings.
When it comes to implementing deep learning technology, he asked: Why is there a gap between expectations and reality?
Here are three common misconceptions Karthikesalingam said must be addressed.
1. More data is all you need for a better model.
Assessing Regulatory Fairness Through Machine Learning Details
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The perils of machine learning – using computers to identify and analyze data patterns, such as in facial recognition software – have made headlines lately.
The perils of machine learning – using computers to identify and analyze data patterns, such as in facial recognition software – have made headlines lately. Yet the technology also holds promise to help enforce federal regulations, including those related to the environment, in a fair, transparent way, according to a new study(link is external) by Stanford researchers.
The analysis, published this week in the proceedings of the Association of Computing Machinery Conference on Fairness, Accountability and Transparency(link is external), evaluates machine learning techniques designed to support a U.S. Environmental Protection Agency (EPA) initiative to reduce severe violations of the Clean Water Act. It reveals how t
Assessing regulatory fairness through machine learning eurekalert.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from eurekalert.org Daily Mail and Mail on Sunday newspapers.
December 21, 2020
What makes people good at having conversations? In a recent paper, Cornell researchers explored conversations on a crisis text service in order to figure out how to answer that question.
“The problem we always came up with was that we never knew if the things we observed were correlations, or if they could actually provide useful information to inform how the platform assigns counselors,” said Justine Zhang, doctoral student in information science and first author of “Quantifying the Causal Effects of Conversational Tendencies.”
The paper was presented at the Association for Computing Machinery Conference on Computer-Supported Cooperative Work and Social Computing, held virtually Oct. 17-21.