How does AI-generated imagery affect photography competitions and trust?

Numbers match the citations in the article. A working link isn't proof that a page supports a claim; check the quoted passage and date.

1. 1

   Prize-winning image which sparked backlash was AI-generated, Nikon rules

   BBC NewsPublished Oct 9, 2026Checked Oct 10, 2026

   “The camera-maker says it is now re-evaluating the rules and procedures of its Small World in Motion contest.”

2. 2

   Text-to-image model (Wikipedia)

   WikipediaPublished Oct 8, 2026Checked Oct 10, 2026

   “A text-to-image (T2I or TTI) model is a machine learning model which takes an input natural language prompt and produces an image matching that description. Text-to-image models gradually began to be developed in the mid-2010s during the beginnings of the AI boom, as a result of advances in deep neural networks. In 2022, the output of state-of-the-art text-to-image models—such as OpenAI's DALL-E 2, Google Brain's Imagen, Stability AI's Stable Diffusion, Midjourney, and Runway's Gen-4—began to be considered to approach the quality of real photographs and human-drawn art. Text-to-image models are generally latent diffusion models, which perform the diffusion process in a compressed latent space rather than directly in pixel space. An autoencoder (often a variational autoencoder) is used to convert between pixel space and this latent representation. These systems typically use a pretrained language or vision–language model to convert the input prompt into a text embedding, and a diffusion-based generative image model that produces images conditioned on that embedding. The most effective models have generally been trained on massive amounts of image and text data scraped from the web.”

3. 3

   Eye Tracking and AI-Generated Content: A Systematic Literature Review of Visual Attention, Cognitive Processing, and User Engagement.

   Journal of eye movement research (Resulbegoviq et al.)Published Sep 4, 2026Checked Oct 10, 2026

   “The evidence covered textual, static visual, audiovisual, and interactive outputs. Across heterogeneous designs and tasks, no modality-independent gaze pattern emerged. AI-generated material sometimes attracted more focal inspection, sometimes received less task-relevant attention, and often redistributed gaze across interface elements. Where supported by task characteristics or complementary outcomes, longer viewing was more often associated with processing difficulty, uncertainty, or checking than with preference. Generated summaries supported learning in some settings, whereas realistic synthetic media remained difficult to identify despite focused inspection. Methodological appraisal identified recurrent limitations in sampling, stimulus matching, confounder control, eye-tracking reporting, and documentation of model versions, prompts, generation settings, and output selection. Observed gaze differences were context-dependent and varied with modality, task, comparator, source belief, expertise, output quality, and measurement choices. Standardized reporting and stronger links between gaze and functional outcomes are needed for cumulative inference.”

4. 4

   Can deepfakes manipulate us? Assessing the evidence via a critical scoping review.

   PloS one (Ching et al.)Published May 2, 2025Checked Oct 10, 2026

   “The potential of this technology to defame and cause harm is clear. However, despite the grave concerns expressed about deepfakes, these concerns are rarely accompanied with empirical evidence. We present a scoping review of the existing empirical studies that aim to investigate the effects of viewing deepfakes on people's beliefs, memories, and behaviour. Five databases were searched, producing an initial sample of 2004 papers, from which 22 relevant papers were identified, varying in methodology and research methods used. Overall, we found that the early studies on this topic have often produced inconclusive findings regarding the existence of uniquely persuasive or convincing effects of deepfake exposure. Moreover, many experiments demonstrated poor methodology and did not include a non-deepfake comparator (e.g., text-based misinformation). We conclude that speculation and scare mongering about dystopian uses of deepfake technologies has far outpaced experimental research that assess these harms. We close by offering insights on how to conduct improved empirical work in this area.”

5. 5

   AI slop (Wikipedia)

   WikipediaPublished Oct 10, 2026Checked Oct 10, 2026

   “AI slop, also known as slop content or simply slop, is digital content made with generative artificial intelligence that is perceived as lacking in effort, quality, or meaning, and usually produced in high volume to gain advantage, to earn money, or to deceive people. It is a form of synthetic media usually linked to the monetization in the creator economy of social media and online advertising. Coined in the 2020s, the term has a pejorative connotation similar to spam. "Slop" was selected as the 2025 Word of the Year by both Merriam-Webster and the American Dialect Society. AI slop has been defined as "digital clutter", "filler content prioritizing speed and quantity over substance and quality", and "shoddy or unwanted AI content in social media, art, books [and] search results". Jonathan Gilmore, a philosophy professor at the City University of New York, describes the material as having an "incredibly banal, realistic style" that is easy for the viewer to process. The effect of increasing AI slop on the web has been likened to the increase in entropy as driven by the Second Law of Thermodynamics.”