Sign In

Communications of the ACM


Formalizing Fairness

balance scale, illustration

Credit: inimalGraphic

As machine learning has made its way into more and more areas of our lives, concerns about algorithmic bias have escalated. Machine learning models, which today facilitate decisions about everything from hiring and lending to medical diagnosis and criminal sentencing, may appear to be data-driven and impartial, at least to naïve users—but the typically opaque models are only as good the data they are trained on, and only as ethical as the value judgments embedded in the algorithms.

The burgeoning field of algorithmic fairness, part of the much broader field of responsible computing, is aiming to remedy the situation. For several years now, along with philosophers, legal scholars, and experts in other fields, computer scientists have been tackling the issue. As Stanford University computer science professor Omer Reingold likes to put it, "We are part of the problem, and we should be part of the solution."


No entries found

Log in to Read the Full Article

Sign In

Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber.

Need Access?

Please select one of the options below for access to premium content and features.

Create a Web Account

If you are already an ACM member, Communications subscriber, or Digital Library subscriber, please set up a web account to access premium content on this site.

Join the ACM

Become a member to take full advantage of ACM's outstanding computing information resources, networking opportunities, and other benefits.

Subscribe to Communications of the ACM Magazine

Get full access to 50+ years of CACM content and receive the print version of the magazine monthly.

Purchase the Article

Non-members can purchase this article or a copy of the magazine in which it appears.