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Garbage in, garbage out. People have long used that expression to describe how poor-quality inputs – whether data, food or anything else – tend to produce bad outputs. And it’s as true for generative artificial intelligence as for anything else. With AI, the main concern I’ve read about is how to avoid infecting the machine with human biases such as racial and gender prejudices. But there’s another source for errors in human judgment: noise. And that data is being fed into AI systems.

Noise refers to variation in how people make judgments of the same problem or situation, explains Mayank Kejriwal, a computer scientist at the University of Southern California, in one of the articles published last week that our regular readers most enjoyed. Well-researched examples include judges issuing different judgments for a similar case – sometimes depending on the weather or the winner of a local football game – and insurance adjusters offering different estimates for comparable claims. And if noise is in the human-generated data, that same noise will likely end up in ChatGPT or any other generative AI program, resulting in messy answers to even seemingly commonsense questions.

“Researchers still don’t know whether or how to weigh AI’s answers in that situation,” Kejriwal writes, “but a first step is acknowledging that the problem exists.”

Bryan Keogh

Managing Editor

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‘Noise’ in the machine: Human differences in judgment lead to problems for AI

Mayank Kejriwal, University of Southern California

Just as human biases show up in machine learning systems, so, too, do people’s vagaries and vicissitudes.

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