Evidence map›Paper›PMID 37307057›Full record

ArticleAccountability in research2024

Let's be fair. What about an AI editor?

Xiaoxu Ling, Siyuan Yan

Abstract readLetterComment
PubMed Publisher
In one paragraph

Article in Accountability in research, 2024. 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. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Xiaoxu LingInstitute of Accounting and Finance, School of Accountancy, Shanghai University of Finance and Economics, Shanghai, China.
Siyuan YanSchool of Business, East China University of Science and Technology, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Much of the current attention on artificial intelligence (AI)-based natural language processing (NLP) systems has focused on research ethics and integrity but neglects their roles in the editorial and peer-reviewing process. We argue that the academic community needs to develop and apply a consistent end-to-end policy on the ethics and integrity of NLP in academic publishing: standards such as drafting requirements and disclosure criteria imposed on potential contributors should be consistently applied to the editorial and peer review process in academic publications.

Indexed as

Artificial IntelligenceEditorial PoliciesNatural Language ProcessingHumansPeer Review, ResearchPeriodicals as TopicPublishingAIChatGPTeditorial policyNLPresearch ethics

Identifiers

PMID37307057

What OpenQuestion holds

Textmetadata
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.