ArticleAI and ethics2025
Legal and ethical implications of AI-based crowd analysis: the AI Act and beyond.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.