EurekAlert!

Injecting fairness into machine-learning models

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 ...
Business Wire

Exactech Publishes New Machine Learning Research That Evaluates Fairness and Accuracy of AI Predictions

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 ...
Nature

FairFML: fair federated machine learning with a case study on reducing gender disparities in cardiac arrest outcome prediction

Health equity is a critical concern in clinical research and practice, as biased predictive models can exacerbate disparities in clinical decision-making and patient outcomes. As healthcare systems ...
Nextgov

Building fairness into AI is crucial – and hard to get right

Get the latest federal technology news delivered to your inbox. With this transformative power, however, comes a significant responsibility: the need to ensure that these technologies are developed ...
Harvard Business School

Rawlsian Fairness for Machine Learning

Joseph, Matthew, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Rawlsian Fairness for Machine Learning." Paper presented at the 3rd Workshop on Fairness, Accountability, and ...
Harvard Business School

An Empirical Study of Rich Subgroup Fairness for Machine Learning

Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, ...
Science Daily

Assessing regulatory fairness through machine learning

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 ...
Nature

Stable learning establishes some common ground between causal inference and machine learning

Machine learning has been incorporated to make predictions within a wide variety of digital services, ranging from search engines to e-commerce to social media platforms, thereby nurturing the booming ...
Science Daily

Machine learning can be fair and accurate

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. Carnegie Mellon University ...
Diginomica

Is fairness in AI a practical possibility? A new angle on designing ethical systems

Is fairness in AI an (im)possibility? Not necessarily, but it requires a proper definition of fairness, in the context of systems design. Here's a fresh look into the ...
San Francisco Fed

Advancing Fairness in Lending Through Machine Learning

Advances in machine learning (ML) provide the opportunity to improve predictions that may expand credit access to more applicants. However, there is concern that gains from advanced models could ...