In the United States, the National Transportation Safety Board is widely respected for its prompt responses to investigate plane, train, and boat accidents. Its independent reports have done much to promote safety in civil aviation and beyond. Could a National Algorithms Safety Board have a similar impact in increasing safety for algorithmic systems, especially the rapidly proliferating Artificial Intelligence applications based on unpredictable machine learning? Alternatively, could agencies such as the Food & Drug Administration (FDA), Securities and Exchange Commission (SEC), or Federal Communications Commission (FCC) take on the task of increasing safety of algorithmic systems? In addition to federal agencies, could the major accounting firms provide algorithmic audits as they do in auditing financial statements of publicly listed companies?

A woman's job application is rejected because of a recruiting algorithm that favors men's résumés. A girl dies by suicide after graphic images of self-harm are pushed up on her feed by social media algorithms. A black teen steals something and gets rated high-risk for committing future crime by an algorithm used in courtroom sentencing, while a white man steals something of similar value and gets rated low-risk. In recent years, advances in computer science have yielded algorithms so powerful that their creators have presented them as tools that can help us make decisions more efficiently and impartially. But the idea that algorithms are unbiased is a fantasy; in fact, they still end up reflecting human biases.

It's convenient when Facebook can tag your friends in photos for you, and it's fun when Snapchat can apply a filter to your face. Both are examples of algorithms that have been trained to recognize eyes, noses, and mouths with consistent accuracy. When these programs are wrong--like when Facebook mistakes you for your sibling or even your mom--it's hardly a problem. In other situations, though, we give artificial intelligence much more responsibility, with larger consequences when it inevitably backfires. Ben Shneiderman, a computer scientist from the University of Maryland, thinks the risks are big enough that it's time to for the government to get involved. In a lecture on May 30 to the Alan Turing Institute in London, he called for a "National Algorithm Safety Board," similar to the US's National Transportation Safety Board for vehicles, which would provide both ongoing and retroactive oversight for high-stakes algorithms.