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 ...
Nature

Improving medical machine learning models with generative balancing for equity and excellence

Weakening the dataset. Throwing away, repeating, or using a subset of data all limit the distribution of data provided to the model and prevent them from learning the optimal patterns present in the ...
Science Daily

When allocating scarce resources with AI, randomization can improve fairness

Researchers argue that, in some situations where machine-learning models are used to allocate scarce resources or opportunities, randomizing decisions in a structured way may lead to fairer outcomes.
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 ...
techtimes

Researchers Develop ‘Fairness’ Algorithms to Reduce AI Bias, Ensure Privacy

Bias in algorithms have always been a key issue in the growing field of artificial intelligence. For instance, researchers found in an experiment that flawed AI in robots has the tendency to make ...
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 ...
AlphaGalileo

Fairness or folly? Global competition exposes critical blind spots in ai deepfake detection

DeepFake technology has grown so sophisticated that AI-generated faces can now fool both human eyes and many detection ...