Abstract
A learner is engaged in “confident learning” when we are guaranteed that the hypotheses put out by the learner will converge in the limit. Whence the word “confidence”? We are confident that the learner will eventually learn. The question raised in the paper is: what does the learner really learn? Friend applies the Putnam permutation argument to the scenario of confident learning to undermine our confidence.
It is true that the learner learns an equivalent algorithm to that put out by the informant. However, it only has to be equivalent in some respects, in order to converge. That is, the algorithm does not have to be identical to the algorithm put out by the informant. As a result, the learning is somewhat superficial. Our confidence can only be placed at a superficial level of learning, which might be alright for some purposes, but not for others.
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Friend, M. (2007). Some Philosophical Concerns about the Confidence in ‘Confident Learning’. In: Friend, M., Goethe, N.B., Harizanov, V.S. (eds) Induction, Algorithmic Learning Theory, and Philosophy. Logic, Epistemology, and the Unity of Science, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6127-1_7
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DOI: https://doi.org/10.1007/978-1-4020-6127-1_7
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