Explaining AI Decisions Using Efficient Methods for Learning Sparse Boolean Formulae
| Autor: | Alessandro Pinto, Michael Francis, Susmit Jha, Tuhin Sahai, Vasumathi Raman |
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| Rok vydání: | 2018 |
| Předmět: |
Atomic sentence
Binary search algorithm Vocabulary Theoretical computer science Computer science media_common.quotation_subject Intelligent decision support system 020207 software engineering 0102 computer and information sciences 02 engineering and technology Formal methods 01 natural sciences Oracle Set (abstract data type) Computational Theory and Mathematics 010201 computation theory & mathematics Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Hamming space Software media_common |
| Zdroj: | Journal of Automated Reasoning. 63:1055-1075 |
| ISSN: | 1573-0670 0168-7433 |
| Popis: | In this paper, we consider the problem of learning Boolean formulae from examples obtained by actively querying an oracle that can label these examples as either positive or negative. This problem has received attention in both machine learning as well as formal methods communities, and it has been shown to have exponential worst-case complexity in the general case as well as for many restrictions. In this paper, we focus on learning sparse Boolean formulae which depend on only a small (but unknown) subset of the overall vocabulary of atomic propositions. We propose two algorithms—first, based on binary search in the Hamming space, and the second, based on random walk on the Boolean hypercube, to learn these sparse Boolean formulae with a given confidence. This assumption of sparsity is motivated by the problem of mining explanations for decisions made by artificially intelligent (AI) algorithms, where the explanation of individual decisions may depend on a small but unknown subset of all the inputs to the algorithm. We demonstrate the use of these algorithms in automatically generating explanations of these decisions. These explanations will make intelligent systems more understandable and accountable to human users, facilitate easier audits and provide diagnostic information in the case of failure. The proposed approach treats the AI algorithm as a black-box oracle; hence, it is broadly applicable and agnostic to the specific AI algorithm. We show that the number of examples needed for both proposed algorithms only grows logarithmically with the size of the vocabulary of atomic propositions. We illustrate the practical effectiveness of our approach on a diverse set of case studies. |
| Databáze: | OpenAIRE |
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