Automating Misrecognition: The Case of Disability.

Jackie Leach Scully, Georgia van Toorn, Sandra Gendera

Abstract read

In one paragraph

Article in Journal of bioethical inquiry, 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

3 authors.

Jackie Leach ScullyDisability Innovation Institute, UNSW Sydney, Goodsell Building, Kensington, NSW 2052, Australia. jackie.leach.scully@unsw.edu.au.ORCID 0000-0002-7637-1225

Georgia van ToornSchool of Social Sciences, UNSW Sydney, Morven Brown Building, Kensington, NSW 2052, Australia.

Sandra GenderaSocial Policy Research Centre, UNSW Sydney, Goodsell Building, Kensington, NSW 2052, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the last decade, bioethics has begun to address the ethical issues emerging as artificial intelligence (AI) and associated technological processes such as automated decision-making (ADM) become part of healthcare and research. Recent work on justice in AI demonstrates that supposedly neutral AI systems can perpetuate the marginalization of various communities. But so far, there has been little exploration of the interaction of AI and disability. In this empirically based project, we have explored the implications of ADM in the lives of people with disability in Australia. This paper focuses on a point that was consistently raised in discussion by disabled participants but is rarely encountered in the AI ethics literature, especially in relation to disability: the problem of automated systems' failures of recognition.

Indexed as

Artificial IntelligenceAutomationDecision MakingPersons with DisabilitiesAustraliaHumansArtificial intelligenceAutomated decision-makingDisabilityDiscriminationEpistemic justiceHuman in the loopRecognition

Identifiers

PMID40459826

DOI10.1007/s11673-025-10462-3

PMCPMC12575558

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.