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Carnegie Mellon University researchers are challenging a long-held assumption that there is a trade-off between accuracy and fairness when using machine learning to make public policy decisions.
Artificial neural networks with human brain connectivity performed cognitive memory tasks more flexibly and efficiently than other systems, researchers say.
University of Washington researchers discovered that AI models ignored clinically significant indicators on X-rays and relied instead on characteristics such as
Credit: Ben Wigler/CSHL, 2021 You would not be surprised to see an elephant in the savanna or a plate in your kitchen. Based on your prior experiences and knowledge, you know that is where elephants and plates are often to be found. If you saw a mysterious object in your kitchen, how would you figure out what it was? You would rely on your expectations or prior knowledge. Should a computer approach the problem in the same way? The answer may surprise you. Cold Spring Harbor Laboratory Professor Partha Mitra described how he views problems like these in a "Perspective" in
E-Mail Artificial intelligence promises to be a powerful tool for improving the speed and accuracy of medical decision-making to improve patient outcomes. From diagnosing disease, to personalizing treatment, to predicting complications from surgery, AI could become as integral to patient care in the future as imaging and laboratory tests are today. But as University of Washington researchers discovered, AI models -- like humans -- have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical setti...