ECO PRO continued
from nature means fewer people have enough direct experience to judge whether a scene is plausible, making them more sus- ceptible to being misled. Under these conditions, misinformation is not just ‘wrong information.’ It becomes a substitute reality, one that can be produced at scale, tailored to audience preferences, and spread faster than corrections. In “Why Take Bad Pictures?” I argued that our industry’s obsession with perfect underwater images can inadvertently mislead: “In striving to create a beautiful image, one can convey the false impression that the aquatic world is doing just fine… breathtaking images often do not reflect the reality of an often-degraded ecosystem just outside the viewfinder.” My proposal was straightforward: celebrate beauty, but pur- posefully include ‘bad pictures,’ images of coral bleaching, disease, ghost nets, plastic pollution, dynamite damage, and
actually are, reducing the sense of urgency, donations, and volunteer efforts. In essence, fakes not only create false beliefs but can also drain motivation and weaken real-world conser- vation efforts. Practical Guidelines for Dive Professionals: For those of us managing dive centers, liveaboards, training agencies, and media outlets, these issues are not just theoretical. They influence how we promote destinations, educate guests, support citizen science, and build, or weaken, trust. Here are some ways we can address these concerns: 1. Create and promote an ‘authenticity code’ (similar to the UK’s Mammal Society’s rule to reject any AI-gener- ated nature images). The code should specify: Ensure that images used in training materials, briefings, ▪ and conservation campaigns are authentic and not AI- generated composites. If AI is used for illustration, such as conceptual graphics, ▪ clearly indicate it as AI-generated. This supports the in- creasing focus on identifying AI-created content. Avoid using AI to artificially enhance underwater wildlife ▪ behavior that never actually occurred or to manipulate the context, such as moving animals closer to humans, chang- ing species mix, or 'cleaning up’ background damage. This is not anti-technology. The guiding principle should be: When it comes to wildlife imagery, AI is for analysis, not for fabrication. 2. Make ‘Bad Pictures’ a systematic part of your visual cur- riculum, with verification in mind. Build a conservation photography library documenting: Coral bleaching and disease ▪ Physical damage (anchors, fins, storms) ▪ Marine debris and ghost gear ▪ Overfishing and loss of apex predators ▪ Coastal development impacts and sedimentation. ▪ But in an era of skepticism, we must also build a verification library: Keep original RAW files and metadata; be ready to present ▪ unedited versions in professional or policy settings. When contributing to citizen science platforms, carefully ▪ follow verification and metadata protocols. As Dr. Guer- rero-Casado warns, distorted or incorrect depictions can undermine species identification; that risk is amplified when the public, and even datasets, are flooded with plau- sible fakes. During briefings and post-dive talks, explain why you’re ▪ showing less-than-pretty images. Link them clearly to cli- mate change, overfishing, and pollution, and to actions guests can take.
overfished reefs. These images make our sto- rytelling more honest and more impactful for conservation. However, the AI era demands a further step. It’s no longer enough for our images to be accurate; they must also be trusted as real. When AI can generate both 'bad’ and ‘good’ scenes from a text prompt, au- thentic conservation photography, and the dive professionals who create it, becomes even more valuable. But so
Real or AI Generated? (it’s AI)
does our responsibility. As Butler warns, the spread of fake imagery imposes insti- tutional costs: “Government agencies and conservation groups are forced to divert time and resources to debunking viral content… As manipulated imagery becomes more common, genuine evidence, from camera traps to field photographs and documented encounters, may be met with skepticism.” Guerrero-Casado and colleagues emphasize an important issue: AI-generated wildlife content can also distort public perceptions of abundance and vulnerability. If digital platforms are filled with realistic yet fake portrayals, people might believe that threatened species are faring better than they
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