Evidence map›Paper›PMID 40421374›Full record

ArticleAI and ethics2025

Legal and ethical implications of AI-based crowd analysis: the AI Act and beyond.

Emmeke Veltmeijer, Charlotte Gerritsen

Abstract read
In one paragraph

Article in AI and ethics, 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

2 authors.

Emmeke VeltmeijerDepartment of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands.ORCID 0000-0002-0749-4520
Charlotte GerritsenDepartment of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands.ORCID 0000-0002-5911-6353

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing global population and the consequent rise in crowded environments have amplified the risks of accidents and tragedies. This underscores the need for effective crowd management strategies, with Artificial Intelligence (AI) holding potential to complement traditional methods. While AI offers promise in analysing crowd dynamics and predicting escalations, its deployment raises significant ethical concerns, regarding privacy, bias, accuracy, and accountability. This paper investigates the legal and ethical implications of AI in automated crowd analysis, with a focus on the European perspective. We examine the effect of the GDPR and the recently accepted AI Act on the field. The study then delves into remaining concerns post-legislation and proposes recommendations for ethical deployment. Key findings highlight challenges in notifying individuals of data usage, protecting vulnerable groups, balancing privacy with safety, and mitigating biased outcomes. Recommendations advocate for non-invasive data collection methods, refraining from predicting and decision-making AI systems, contextual considerations, and individual responsibility. The recommendations offer a foundational framework for ethical AI deployment, with universal applicability to benefit citizens globally.

Indexed as

AI ActAI-based crowd analysisEthical implicationsGDPRPractical recommendations

Identifiers

PMID40421374
PMCPMC12103326

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.