New research compares the performance of white-box artificial intelligence (AI) models with their black-box counterparts to identify which is more accurate. In simple terms, the difference between white and black box models relates to the complexity of the rules and parameters used to inform their outputs. White box models contain only a few straightforward rules with limited parameters, making them more transparent and easier to limit biases. On the other hand, black box models can have thousands of rules and even billions of parameters. But which approach works best?
According to the research, "in many cases, simple, interpretable AI models perform just as well as black box alternatives — without sacrificing the trust of users or allowing hidden biases to drive decisions."
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