If a machine-learning model is trained using an unbalanced dataset, such as one that contains far more images of people with lighter skin than people with darker skin, there is serious risk the ...

Bias in AI is a truly worrisome issue. We’ve seen algorithms that are racist, sexist, and every other negative -ist you can think of. Even more troubling: if we eliminate all the human bias in our ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...

GAINESVILLE, Fla.--(BUSINESS WIRE)--Exactech, a developer and producer of innovative implants, instrumentation, and smart technologies for joint replacement surgery, reports a new study 1 that ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

Physicians and medical experts are starting to incorporate algorithms and machine learning in many parts of the health care system, including experimental models to analyze images from X-rays and ...

Unfairly trained Artificial Intelligence (AI) systems can reinforce bias, therefore AI systems must be trained fairly. Experts say AI fairness is a dataset issue for each specific machine learning ...

Japan's leading developer of new AI technologies Fujitsu accelerates its commitment to open source innovation with new projects hosted in LF AI & Data Fujitsu accelerates its commitment to open source ...

Scientists have introduced a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints. Seventy years ago, ...

Applying machine learning to a U.S. Environmental Protection Agency initiative, researchers reveal how key design elements determine what communities bear the burden of pollution. The approach could ...