Pith. sign in

REVIEW 1 cited by

Explainable AI: current status and future directions

Not yet reviewed by Pith; the record is open.

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 2107.07045 v1 pith:5GSR45QN submitted 2021-07-12 cs.LG cs.AI

classification cs.LGcs.AI
keywords beentechniquesapplicationsartificialdirectionsexplainabilityexplainablefuture
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions. This explainability is not possible in traditional AI. Explainability is essential for critical applications, such as defense, health care, law and order, and autonomous driving vehicles, etc, where the know-how is required for trust and transparency. A number of XAI techniques so far have been purposed for such applications. This paper provides an overview of these techniques from a multimedia (i.e., text, image, audio, and video) point of view. The advantages and shortcomings of these techniques have been discussed, and pointers to some future directions have also been provided.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 78 citations worldwide. Full citation record

  1. fSRD: Fuzzy Spectral Region Decomposition -- Automated Multi Operator Koopman Representations via an Adaptive Spectral Learning Architecture

    cs.LG 2026-07 conditional novelty 6.0 of 10

    fSRD automates multi-operator Koopman modelling by fitting local DMD models inside adaptively learned fuzzy regions of a data matrix, reporting high in-sample reconstruction accuracy on chaotic and high-dimensional data.

Pith tools