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 ...

A computerized matching system can be designed to be fair and still produce unequal outcomes if the people using it do not ...

Artificial intelligence holds great promise for improving human health by helping doctors make accurate diagnoses and treatment decisions. It can also lead to discrimination that can harm minorities, ...

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 ...

As organizations increasingly rely on algorithms to rank candidates for jobs, university spots, and financial services, a new method, named hyperFA*IR, offers a more principled approach when picking ...

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 ...

Accenture’s new Fairness Tool is a way to quickly evaluate whether your data is creating fair outcomes. “I’m hoping is that this is something that we can make accessible and available and easy to use ...

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 ...

In the wake of recent high-profile AI bias scandals, companies have begun to realize that they need to rethink their AI strategy to include not just AI Fairness, but also Algorithmic Fairness more ...