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Towards Explainable Neural-Symbolic Visual Reasoning

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arxiv 1909.09065 v2 pith:MDXQB66V submitted 2019-09-19 cs.LG cs.AI

Adrien Bennetot , Jean-Luc Laurent , Raja Chatila , Natalia Díaz-Rodríguez This is my paper · ORCID
classification cs.LGcs.AI
keywords arxivdecisionexplainableexplanationsmodelnetworkorderproduce
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Many high-performance models suffer from a lack of interpretability. There has been an increasing influx of work on explainable artificial intelligence (XAI) in order to disentangle what is meant and expected by XAI. Nevertheless, there is no general consensus on how to produce and judge explanations. In this paper, we discuss why techniques integrating connectionist and symbolic paradigms are the most efficient solutions to produce explanations for non-technical users and we propose a reasoning model, based on definitions by Doran et al. [2017] (arXiv:1710.00794) to explain a neural network's decision. We use this explanation in order to correct bias in the network's decision rationale. We accompany this model with an example of its potential use, based on the image captioning method in Burns et al. [2018] (arXiv:1803.09797).

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  1. Learning Predictive Checklists with Probabilistic Logic Programming

    cs.LG 2024-11 conditional novelty 5.0 of 10

    ProbChecklist learns predictive checklists end to end from images, time series, and text by treating learned concept probabilities as probabilistic facts in a checklist logic program.

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