The accuracy and robustness of computational models is only one side of the equation. The field of algorithmic fairness and accountability investigates the decision-making capabilities of data-driven ...

This study compared 6 algorithmic fairness–improving approaches for low-birth-weight predictive models and found that they improved accuracy but decreased sensitivity for Black populations. Objective: ...

In total, 5,708 patients from five randomized phase III trials were included. Two MMAI algorithms were evaluated: (1) the distant metastasis (DM) MMAI model optimized to predict risk of DM, and (2) ...

When natural disasters or extreme weather events hit, delivering aid quickly and efficiently to those affected is crucial. Humanitarian relief efforts commonly rely on the combination of trucks and ...

This research area examines how individuals perceive fairness in algorithmic decision-making and how these perceptions affect the acceptance and adoption of AI systems. We investigate various fairness ...

BKC Faculty Associate Ben Green writes about the challenge of creating equitable policy reforms around algorithmic fairness. “Efforts to promote equitable public policy with algorithms appear to be ...

Part 2, Digital Inequality Series: Under what conditions can artificial intelligence benefit all of society vs. just a few people? Kalinda Ukanwa, a quantitative marketing scholar at the University of ...

As algorithmic decision-making becomes increasingly pervasive, it raises challenging issues pertaining to equality and equity. This timely discussion on fairness and technology is grounded in ...

As price-setting by computer algorithm becomes increasingly prevalent, states are stepping in to address transparency and fairness concerns that federal legislation has yet to comprehensively tackle.