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Towards Grad-CAM Based Explainability in a Legal Text Processing Pipeline

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arxiv 2012.09603 v1 pith:ZHLM7TZO submitted 2020-12-15 cs.HC

Lukasz Gorski , Shashishekar Ramakrishna , Jedrzej M. Nowosielski This is my paper · ORCID
classification cs.HC
keywords explainabilitygrad-camprocessingdomainlegaltransparencyabilityadapted
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Explainable AI(XAI)is a domain focused on providing interpretability and explainability of a decision-making process. In the domain of law, in addition to system and data transparency, it also requires the (legal-) decision-model transparency and the ability to understand the models inner working when arriving at the decision. This paper provides the first approaches to using a popular image processing technique, Grad-CAM, to showcase the explainability concept for legal texts. With the help of adapted Grad-CAM metrics, we show the interplay between the choice of embeddings, its consideration of contextual information, and their effect on downstream processing.

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  1. EVolutionary Independent DEtermiNistiC Explanation

    cs.LG 2025-01 reject novelty 4.0 of 10

    EVIDENCE is a stochastic frequency-band masking method for explaining audio classifiers, but its reported gains are inflated by using test labels to select the masks.

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