Three advantages of non-AI models

It’s hard to imagine doing anything like Midjourney’s image generation without neural networks. The same is true of ChatGPT’s text generation. But a lot of business tasks do not require AI, and in fact would be better off not using AI. Three reasons why:

  1. Statistical models are easier to interpret. When a model has a dozen parameters, you can inspect each one. When a model has a billion parameters, not so much.
  2. Statistical models are more robust. Neural networks perform well on average, but fail in bizarre ways, and there is no way to explore all the possible edge cases. The edge cases of a statistical model can be enumerated, examined, and mitigated.
  3. Statistical models are not subject to legislation hastily written in response to recent improvements in AI. The chances that such legislation will have unintended consequences are roughly 100%.

3 thoughts on “Three advantages of non-AI models

  1. #1 is exactly what I teach my stats undergrads by comparing various classification models. Only LDA and CART give “explainable” classifiers, with CART being the more explicit.

  2. Erwin Kalvelagen

    In economic models, a good reason to use some specific functional form is the availability of some well-established theory. You can expect questions when ignoring that.

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