Could 'expiration dates' for AI systems help prevent bias?

Today's AI technology, much like humans, learns from examples. AI systems are developed on datasets containing text, images, audio, and other information that serve as a ground truth. By figuring out the relationships between these examples, AI systems gradually "learn" to make predictions, like which word is likely to come next in a sentence or whether objects in a picture are inanimate. The technique holds up remarkably well in the language domain, for example, where systems like OpenAI's GPT-3 can write content from essays to advertisements in human-like ways. But similar in character to humans, AI that isn't supplied fresh, new data eventually grows stale in its predictions -- a phenomenon known as model drift.