Evidence map›Paper›PMID 38136454›Full record

ArticleEntropy (Basel, Switzerland)2023

Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning.

Emanuele Marconato, Andrea Passerini, Stefano Teso

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Emanuele MarconatoDipartimento di Ingegneria e Scienza dell'Informazione, University of Trento, 38123 Trento, Italy.
Andrea PasseriniDipartimento di Ingegneria e Scienza dell'Informazione, University of Trento, 38123 Trento, Italy.
Stefano TesoDipartimento di Ingegneria e Scienza dell'Informazione, University of Trento, 38123 Trento, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research on Explainable Artificial Intelligence has recently started exploring the idea of producing explanations that, rather than being expressed in terms of low-level features, are encoded in terms of

Indexed as

alignmentcausal abstractionscausal representation learningconcept leakagedisentanglementexplainable AI

Identifiers

PMID38136454
PMCPMC10742865

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.