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ExplAIner language offers unified framework for explaining Boolean classification models

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced ExplAIner, a new declarative query language designed to uniformly specify, combine, and analyze various notions for explaining Boolean classification models. This language extends the FOIL framework to address limitations in expressing optimality-based queries and improves evaluation complexity. ExplAIner can express a wide array of explanation types, including abductive, contrastive, and feature-based queries, with its evaluation problem belonging to the Boolean hierarchy. An optimization-oriented fragment, Opt-FOIL, is also presented for computing minimal explanations, with its evaluation problem in FP^NP. AI

IMPACT Provides a unified framework for analyzing and specifying explanations for Boolean models, potentially simplifying XAI research.

RANK_REASON The cluster contains an academic paper detailing a new query language for explaining classification models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ExplAIner language offers unified framework for explaining Boolean classification models

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The cluster contains an academic paper detailing a new query language for explaining classification models.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Marcelo Arenas, Pablo Barcel\'o, Diego Bustamante, Jose Caraball, Mar\'ia Alejandra Schild, Bernardo Subercaseaux ·

    ExplAIner: A Declarative Query Language for Explaining Classification Models

    arXiv:2607.06407v1 Announce Type: new Abstract: The XAI community has studied a wide range of queries and scores for explaining predictions of ML models. From a data management perspective, this proliferation of explanation notions calls for declarative query languages in which s…

  2. arXiv cs.AI TIER_1 English(EN) · Bernardo Subercaseaux ·

    ExplAIner: A Declarative Query Language for Explaining Classification Models

    The XAI community has studied a wide range of queries and scores for explaining predictions of ML models. From a data management perspective, this proliferation of explanation notions calls for declarative query languages in which such notions can be specified, combined, and anal…

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