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Explainable artificial intelligence datasets

archive 2025-07-28

8 datasets carry the task tag "Explainable artificial intelligence" (the task itself: Explainable artificial intelligence), ordered by the archive's paper count. Page 1 of 1: 8 shown of 8. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

Explainable artificial intelligence datasets 1–8 of 8

BDD-X (Berkeley Deep Drive-X (eXplanation))
Berkeley Deep Drive-X (eXplanation) is a dataset is composed of over 77 hours of driving within 6,970 videos.
47 papers · 0 benchmarks
OpenXAI is the first general-purpose lightweight library that provides a comprehensive list of functions to systematically evaluate the quality of explanations generated by attribute-based explanation methods.
17 papers · 0 benchmarks
e-SNLI-VE is a large VL (vision-language) dataset with NLEs (natural language explanations) with over 430k instances for which the explanations rely on the image content.
16 papers · 2 benchmarks
e-ViL is a benchmark for explainable vision-language tasks.
10 papers · 0 benchmarks
XAI-Bench is a suite of synthetic datasets along with a library for benchmarking feature attribution algorithms.
8 papers · 0 benchmarks
For a detailed description, we refer to Section 3 in our research article.
3 papers · 0 benchmarks
EUCA dataset description Associated Paper: EUCA: the End-User-Centered Explainable AI Framework Authors: Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, Ghassan Hamarneh Introduction: EUCA dataset is for modelling personalized or…
1 paper · 0 benchmarks
ExBAN (ExBAN Corpus (Explanations for BAyesian Networks))
The ExBAN dataset: a corpus of NL explanations generated by crowd-sourced participants presented with the task of explaining simple Bayesian Network (BN) graphical representations.
1 paper · 0 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.