Dissecting explanatory power

By Petri Ylikoski, Jaakko Kuorikoski. 2010

240 citations recorded by the source corpus.

Comparisons of rival explanations or theories often involve vague appeals to explanatory power. In this paper, we dissect this metaphor by distinguishing between different dimensions of the goodness of an explanation: non-sensitivity, cognitive salience, precision, factual accuracy and degree of integration. These dimensions are partially independent and often come into conflict. Our main contribution is to go beyond simple stipulation or description by explicating why these factors are taken to be explanatory virtues. We accomplish this by using the contrastive-counterfactual approach to explanation and the view of understanding as an inferential ability. By combining these perspectives, we show how the explanatory power of an explanation in a given dimension can be assessed by showing the range of answers it provides to what-if-things-had-been-different questions and the theoretical and pragmatic importance of these questions. Our account also explains intuitions linking explanation to unification or to exhibition of a mechanism.

Subfields: Artificial Intelligence, History and Philosophy of Science, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b4eb4f6b3082ea1a51e8991868dda0815f13b858

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In Artificial Intelligence

In History and Philosophy of Science

Francesco Guala

In Molecular Biology

Citations in the corpus

A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021, background): “Causal contrastive-counterfactual explanation is sometimes considered to include ‘‘all kinds of subjunc-tive conditionals, regardless of whether the antecedent is true in the actual world or not’’ [126].”
Institutions, rules, and equilibria: a unified theory* (2014, background): “This can be done by increasing explanatory depth or explanatory integration (Ylikoski and Kuorikoski 2012).”
Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics (2021, background): “…strength [23,46] Main effect complexity [46] Level of (dis)agreement [82] Attribution-based explanations Monotonicity [80] Non-sensitivity [80,83], Sensitivity [84,85] Effective complexity [80] Remove and retrain [86] Recall of important features [33] Implementation invariance [85]…”