What Does It Mean When Machine Learning Makes a Mistake?
A comment on my recent post about the public perception of machine learning got me thinking about the meaning of error in machine learning. The reader asked if I thought machine learning models would always “make mistakes”. As I described in that post, people have a strong tendency to anthropomorphize machine learning models. When we interact with an LLM chatbot, we apply techniques to those engagements that we have learned by communicating with other people—persuasion, phrasing, argument, etc. However, this is often ineffective and will result in unsatisfying responses.
In my own day to day work, I see similar sorts of issues related to classifiers and regression models as well. My team and I spend a lot of time and energy trying to help customers and colleagues understand that machine learning is not perfect (and realistically never will be). “Why did the model say X when the truth turned out to be X-5?” is a perpetual theme. I don’t blame the question askers, at least not entirely, because as I wrote in my last piece, we in the broader machine learning community have not done a good job of teaching fundamental ML literacy.
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