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 ...
Who gets the job interview. Who receives public benefits. Who is flagged as high risk. Increasingly, these outcomes are shaped not by human deliberation but by algorithmic systems embedded deep within ...
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 ...
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) ...
“I’m hoping is that this is something that we can make accessible and available and easy to use for non-tech companies–for some of our Fortune 500 clients who are looking to expand their use of AI, ...
In this article, we recognize the profound effects that algorithmic decision-making can have on people's lives and propose a harm-reduction framework for algorithmic fairness. We argue that any ...
Read more about AI may be efficient, but public still prefers humans in scarce resource decisions on Devdiscourse ...
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 ...
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