Towards Explainable Neural-Symbolic Visual Reasoning
arXiv:1909.09065
Abstract
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).
Accepted at IJCAI19 Neural-Symbolic Learning and Reasoning Workshop (https://sites.google.com/view/nesy2019/home)
References in corpus (4)
- Methods for Interpreting and Understanding Deep Neural Networks
- What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
- Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
- Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning