Evidence map›Paper›PMID 33686527›Full record

ArticleScience and engineering ethics2021

Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality.

Mark Ryan, Josephina Antoniou, Laurence Brooks, Tilimbe Jiya, Kevin Macnish, Bernd Stahl

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In one paragraph

Article in Science and engineering ethics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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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

6 authors.

Mark RyanWageningen Economic Research, Wageningen University and Research, Wageningen, The Netherlands. mark.ryan@wur.nl.ORCID 0000-0003-4850-0111
Josephina AntoniouUCLan Cyprus, Larnaka, Cyprus.
Laurence BrooksDe Montford University, Leicester, UK.
Tilimbe JiyaNorthampton University, Northampton, UK.
Kevin MacnishThe University of Twente, Enschede, The Netherlands.
Bernd StahlDe Montford University, Leicester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the ethical use of Big Data and Artificial Intelligence (AI) technologies (BD + AI)-using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues, (from the literature), into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety of different social domains, fields, and applications of AI, there is overlap and correlation between the organisations' ethical concerns. This more detailed understanding of ethics in AI + BD is required to ensure that the multitude of suggested ways of addressing them can be targeted and succeed in mitigating the pertinent ethical issues that are often discussed in the literature.

Indexed as

Artificial IntelligenceBig DataHumansMoralsOrganizationsTechnologyArtificial intelligence ethicsBig data analyticsMultiple-case study analysisPhilosophy of technologySmart information systems

Identifiers

PMID33686527
PMCPMC7977017

What Socratic 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 Socratic graph.