Motivating Transparent Communications about Bias in Healthcare Technology Development.

Anna Tovmasyan, Alice Liefgreen, Sandra Wachter, Brent Mittelstadt, Netta Weinstein

Abstract read

In one paragraph

Article in Collabra. Psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Anna TovmasyanSchool of Psychology and Clinical Language Sciences, University of Reading.

Alice LiefgreenLanguage and Cognition, University College London.

Sandra WachterOxford Internet Institute, University of Oxford.

Brent MittelstadtOxford Internet Institute, University of Oxford.

Netta WeinsteinSchool of Psychology and Clinical Language Sciences, University of Reading.

Funding

Wellcome Trust 223765

6 · The paper itself

Abstract

As healthcare artificial intelligence (AI) systems advance, their capacity for bias (e.g., as a function of patient protected characteristics) increases as well and these limitations are often left undisclosed by developers. Here, the question arises - do supportive motivational messaging designed to increase buy-in inspire healthcare AI developers to transparently communicate about bias in their technology? Computer science students (Study 1:

Indexed as

healthcare AIinclusionmotivationtransparencyvalues

Identifiers

PMID41262305

DOI10.1525/collabra.136456

PMCPMC7618381

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.