Minds and Machines 32 (1):185-218 (2022)

Authors
David Watson
University College London
Luciano Floridi
Oxford University
Abstract
Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence, a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence, we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method’s competitive performance against state of the art XAI tools on a diverse array of tasks.
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DOI 10.1007/s11023-022-09598-7
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References found in this work BETA

Word and Object.Willard Van Orman Quine - 1960 - Cambridge, MA, USA: MIT Press.
The Foundations of Statistics.Leonard J. Savage - 1954 - Wiley Publications in Statistics.
The Logic of Decision.Richard C. Jeffrey - 1965 - New York, NY, USA: University of Chicago Press.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.

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