What makes classification trees comprehensible?

Piltaver, Rok; Luštrek, Mitja; Gams, Matjaž; Martinčić-Ipšić, Sanda

doi:10.1016/j.eswa.2016.06.009

Cited by 47 publications

(21 citation statements)

References 14 publications

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“…The study also does not report whether the participants of their study had any domain knowledge relating to the used data, so that it cannot be ruled out that the obtained result was caused by lack of domain knowledge. 20 A similar study was later conducted by Piltaver et al (2016), who found a clear relationship between model complexity and interpretability in decision trees.…”

Section: Conflicting Evidencementioning

confidence: 76%

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: Conflicting Evidencementioning

confidence: 76%

On cognitive preferences and the plausibility of rule-based models

2019

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“…More recently, Fürnkranz et al (2018) performed an experiment with 390 participants to question the idea that the likeliness that a user will accept a logical model such as rule sets as an explanation for a decision is determined by the simplicity of the model. Lage et al (2019) also explore the complexities of rule sets to find features that make them more interpretable, while Piltaver et al (2016) undertake a similar analysis in the case of classification trees. Another important aspect of this empirical line of research is the study of cognitive biases in the understanding of interpretable models.…”

Section: Alternative Paths To Understandingmentioning

confidence: 99%

The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

Páez

2019

Minds & Machines

163

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In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post-hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post-hoc interpretability that seems to be predominant in most recent literature.

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“…Users (i.e. analysts) trust models/results that explanations can be drawn from, sometimes regardless of their predictive performances [6]. Third, lack of interpretability goes against the principle of ease-of-use -an important success factor of any system design.…”

Section: Complexity-interpretability Trade-offmentioning

confidence: 99%

Dissecting Moneyball: Improving Classification Model Interpretability in Baseball Pitch Prediction

Hickey¹,

Zhou²,

Tao³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Data science, where technical expertise meets domain knowledge, is collaborative by nature. Complex machine learning models have achieved human-level performance in many areas, yet they face adoption challenges in practice due to limited interpretability of model outputs, particularly for users who lack specialized technical knowledge. One key question is how to unpack complex classification models by enhancing their interpretability to facilitate collaboration in data science research and application. In this study, we extend two state-of-the-art methods for drawing finegrained explanations from the results of classification models. The main extensions include aggregating explanations from individual instances to a user-defined aggregation level, and providing explanations with the original features rather than engineered representations. We use the prediction of baseball pitch outcome as a case to evaluate our extended methods. The experiment results of the methods with real sensor data demonstrate their improved interpretability while preserving superior prediction performance.

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What makes classification trees comprehensible?

Cited by 47 publications

References 14 publications

On cognitive preferences and the plausibility of rule-based models

On cognitive preferences and the plausibility of rule-based models

The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

Dissecting Moneyball: Improving Classification Model Interpretability in Baseball Pitch Prediction

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