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1000
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Abstract/Summary
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<jats:p>Abstract. This study examines the risks associated with relying solely on images for documenting new species records, rediscoveries, taxonomic descriptions, and distribution expansions. We highlight concerns regarding image authenticity, especially in cases where images may be altered, adulterated, or AI (artificial intelligence)-generated, potentially leading to inaccuracies in biodiversity documentation. To illustrate the evolving challenges, we conducted an experiment with 621 participants who assessed nine AI-generated images. Surprisingly, six were deemed authentic, while three raised doubts, highlighting the difficulty in discerning AI-generated content. Our main message emphasizes the critical role of trust in biodiversity documentation, particularly for taxonomy and conservation, and how eroded trust can hinder conservation efforts. Improved communication and collaboration between taxonomists and conservationists are needed, emphasizing scientific integrity. We urge a reevaluation of journal policies concerning data validation, especially in articles relying on images as primary evidence, to preserve the credibility of scientific research amidst technological advancements.
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1000
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Hinweis
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DeepGreen-ID: 48fc3474f43f467d948130a4308256cc ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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