Industrial applications of ML: Illustrations for the KAML dilemma and the CBR dream

Kadratoff, Y.

doi:10.1007/3-540-57868-4_47

Cited by 3 publications

(1 citation statement)

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“…Interpretability is a very elusive concept which we use in an intuitive sense. Kodratoff (1994) has already observed that it is an ill-defined concept, and has called upon several communities from both academia and industry to tackle this problem, to "find objective definitions of what comprehensibility is", and to open "the hunt for probably approximate comprehensible learning". Since then, not much has changed.…”

Section: Aspects Of Interpretabilitymentioning

confidence: 99%

On cognitive preferences and the plausibility of rule-based models

2019

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It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that-all other things being equal-longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowdsourcing study based on about 3,000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.

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Section: Aspects Of Interpretabilitymentioning

confidence: 99%