Pith. sign in

REVIEW 2 cited by

Landscape of R packages for eXplainable Artificial Intelligence

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2009.13248 v3 pith:ELQIPV55 submitted 2020-09-24 cs.LG stat.ML

Szymon Maksymiuk , Alicja Gosiewska , Przemyslaw Biecek This is my paper · ORCID
classification cs.LGstat.ML
keywords methodspackagesartificialavailableexplainableintelligencemodelspython
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need methods for exploration, debugging, and validation. New methods and tools for this purpose are being developed within the eXplainable Artificial Intelligence (XAI) subdomain of machine learning. In this work (1) we present the taxonomy of methods for model explanations, (2) we identify and compare 27 packages available in R to perform XAI analysis, (3) we present an example of an application of particular packages, (4) we acknowledge recent trends in XAI. The article is primarily devoted to the tools available in R, but since it is easy to integrate the Python code, we will also show examples for the most popular libraries from Python.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models

    cs.HC 2025-07 conditional novelty 7.0 of 10

    More comprehensible explainability visualizations increase perceived model bias and decrease trust, with bias perception mediating the negative comprehension-trust relationship.

  2. midr: Learning from Black-Box Models by Maximum Interpretation Decomposition

    stat.ME 2025-06 conditional novelty 5.0 of 10

    The midr package builds a global additive surrogate for any black-box model by least-squares projection with strict centering constraints, yielding interpretable main effects, interactions, and SHAP-style attributions.

Pith tools