Evidence map›Paper›PMID 31828533›Full record

ArticleScience and engineering ethics2020

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices.

Jessica Morley, Luciano Floridi, Libby Kinsey, Anat Elhalal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science and engineering ethics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 6 pooled it
–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

92 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
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  12. Clinical AI is Not (Yet) Trustworthy-But It Could Be.Journal of medical Internet research · 2026
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32 more citing papers are in PubMed but not listed here.

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

4 authors.

Jessica MorleyOxford Internet Institute, University of Oxford, 1 St Giles', Oxford, OX1 3JS, UK. Jessica.morley@kellogg.ox.ac.uk.ORCID 0000-0001-5221-4770
Luciano FloridiOxford Internet Institute, University of Oxford, 1 St Giles', Oxford, OX1 3JS, UK.
Libby KinseyDigital Catapult, 101 Euston Road, Kings Cross, London, NW1 2RA, UK.
Anat ElhalalDigital Catapult, 101 Euston Road, Kings Cross, London, NW1 2RA, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741-742, 1960. https://doi.org/10.1126/science.132.3429.741 ; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles-the 'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)-rather than on practices, the 'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

Indexed as

Artificial IntelligenceMachine LearningAnimalsBeneficenceHumansResearch PersonnelSocial JusticeApplied ethicsArtificial intelligenceData governanceDigital ethicsEthics of AIGovernanceMachine learning

Identifiers

PMID31828533
PMCPMC7417387

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.