Algorithms were supposed to make our lives easier and fairer: help us find the best job applicants, help judges impartially assess the risks of bail and bond decisions, and ensure that health care is ...

Algorithms are a staple of modern life. People rely on algorithmic recommendations to wade through deep catalogs and find the best movies, routes, information, products, people and investments.

To combat algorithmic bias in healthcare, including race and ethnicity is critical, a new study says. Algorithms are used to make healthcare decisions, and can often be more accurate than a clinical ...

In recent years, employers have tried a variety of technological fixes to combat algorithm bias — the tendency of hiring and recruiting algorithms to screen out job applicants by race or gender. They ...

Irina Raicu is the director of the Internet Ethics program (@IEthics) at the Markkula Center for Applied Ethics. Views are her own. The following is a lightly edited version of comments made as part ...

Artificial intelligence has become a popular tool for job recruiters, in part because programmers can code applicant-screening algorithms to avoid any explicit discrimination in their decision-making ...

Carey K. Morewedge does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond ...

Algorithms are becoming more entrenched in our lives, a consequence of the growing stores of data and the push to make greater use of them. While that’s happening everywhere, in health care, the ...

It’s no secret that algorithms are incredibly problematic, leading to everything from racist policing to sexist hiring. But even for adults who are extremely online, it can be hard to understand what ...

Algorithms aren’t acting maliciously. They’re doing what they were built to do. That’s why algorithmic bias in marketing is ...

New research shows that people recognize more of their biases in algorithms' decisions than they do in their own -- even when those decisions are the same. Algorithms were supposed to make our lives ...