Artificial intelligence has the potential to impose the values of its creators on its users, those affected by it, and society. Users also may mean to use a technological device in an illicit or unexpected way. Devices change people's intentions as they are empowered by technology. What people mean to do with the help of technology reflects their choices, preferences, and values. Technology is a disruptor that impacts society as a whole.

AI Policy Matters is a regular column in the ACM SIGAI AI Matters newsletter featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog. Facial recognition (FR) issues continue to appear in the news, as well as in scholarly journal articles, while FR systems are being banned and some research is shown to be bad science. AI system researchers who try to associate facial technology output with human characteristics are sometimes referred to as machine-assisted phrenologists. Problems with FR research have been demonstrated in machine learning research such as work by Steed and Caliskan in "A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild." Meanwhile many examples of harmful products and misuses have been identified in areas such as criminality, video interviewing, and many others. Some communities have considered bans.

Companies invested roughly $50 billion in artificial intelligence systems last year. That figure is expected to more than double, to $110 billion, by 2024. Such an explosion in investment raises a lot of questions, but central among them for MIT Sloan senior lecturerRenée Richardson Goslineis how to recognize and counteract the bias that exists within AI-driven decision-making. "There has been a tremendous amount of research pointing out issues of algorithmic bias and the threat this poses systemically," Gosline says in a new MIT Sloan Experts Series talk, available below. "This is a massive issue -- one that I don't think we can take seriously enough."

Machines can now speak, read, retain knowledge, interact with people, and identify objects and patterns. It's no wonder that companies in lots of industries--health care, finance, retail--are pouring billions of dollars into AI. It blurs the lines between capabilities such as knowledge, skills, and processes, for instance, and it changes how companies think about sourcing and sharing data. To gain an advantage from AI, it helps to go back to basics: What do your customers need? How can machines and people work together to meet those needs?