Technology use often goes wrong, Parsons notes, "because we're too focused on either our own ideas of what good looks like or on one particular audience as opposed to a broader audience." That may look like an app developer building only for an imagined customer who shares his geography, education, and affluence, or a product team that doesn't consider what damage a malicious actor could wreak in their ecosystem. "We think people are going to use my product the way I intend them to use my product, to solve the problem I intend for them to solve in the way I intend for them to solve it," says Parsons. "But that's not what happens when things get out in the real world." AI, of course, poses some distinct social and ethical challenges. Some of the technology's unique challenges are inherent in the way that AI works: its statistical rather than deterministic nature, its identification and perpetuation of patterns from past data (thus reinforcing existing biases), and its lack of awareness about what it doesn't know (resulting in hallucinations).