Truace
Subscribe
The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 939 sourced gains and 772 sourced problems, averaging 68 and 66 on the index score. Readers have logged 22 public signals on the Pulse, which is kept apart and never counted as evidence.

939 gains
772 problems
Every sourced claim in the Index, one square each, shaded by the strength of its evidence. High Moderate Emerging

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,711 results
Show filters and sorting

Download every matching row, not just this page:Export CSVExport JSON

AI gains · 939

533
HealthStableModerate evidence · 1 source

Reddit users reported gaining accessible mental health support from ChatGPT, including emotional validation and practical help preparing for therapy and navigating difficult conversations.

Researchers analyzed Reddit posts and comments about mental health conversations with ChatGPT to understand how large language models are being used for support outside clinical settings. By October 2025, they found users described ChatGPT as accessible and non-judgmental, providing emotional support, validation, and practical help like navigating difficult conversations and preparing for therapy.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
87

Updated Aug 7, 2026 · TRV-2026-0681

67Index score
534
ClimateStableModerate evidence · 1 source

Machine learning models combined with fuzzy logic integration improved prediction of flood, avalanche, rockfall and landslide susceptibility in a mountainous region of northern Iran, with RF and AND operator achieving highest reliability for spatial planning.

On 2026-08-06, a peer-reviewed study described an integrated assessment for a mountainous area in northern Iran covering flood, avalanche, rockfall and landslide. Authors trained ANN, RF and SVM models on 21 environmental variables and validated them against field inventories, then combined outputs with fuzzy AND, OR and GAMMA operators to distinguish compound from cumulative hazard zones.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
87

Updated Aug 7, 2026 · TRV-2026-0677

67Index score
535
HealthStableModerate evidence · 1 source

Pooled testing of deep learning models for subdural hematoma detection on non-contrast CT achieved high diagnostic performance, with U-Net models showing significantly higher sensitivity and precision than other architectures.

A single-arm meta-analysis published August 6, 2026 pooled 30 independent test datasets totaling 67,266 non-contrast CT scans to compare convolutional neural networks, U-Net, and hybrid deep learning models for subdural hematoma detection. U-Net models demonstrated significantly higher sensitivity and precision, while all architectures showed consistently high specificity, diagnostic odds ratio, and accuracy.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
87

Updated Aug 7, 2026 · TRV-2026-0676

67Index score
536
EducationStableModerate evidence · 1 source

Final-year dental students reported frequent LLM use to save time and support learning, including clarifying and understanding complex concepts.

A cross-sectional survey of 454 final-year dental students in the UAE, Jordan, Malaysia, Oman, and Brazil examined LLM use, motivations, and safeguards. Published August 6 2026, it found ChatGPT predominated at 95.9%, with 39.2% using LLMs several times per week and 28.6% daily for tasks like understanding complex concepts and summarising lecture notes.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
87

Updated Aug 7, 2026 · TRV-2026-0675

67Index score

AI problems · 772

533
ScienceStableModerate evidence · 1 source

Autonomous research systems could overwhelm peer review infrastructure and pollute scientific literature with low-quality or noisy papers.

Researchers built The AI Scientist, an agentic system using foundation models to automate conception, coding, experimentation, data analysis, manuscript writing, and peer review. By March 2026 they reported that a manuscript fully generated by the system passed first-round review for a workshop at a top-tier machine learning conference.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0163

67Index score
534
LaborStableModerate evidence · 1 source

Relying on passive AI copying at work reduced self-efficacy, psychological ownership, and work meaningfulness, with declines in efficacy and meaningfulness persisting after returning to manual work.

Researchers ran a pre-registered lab experiment with 269 participants doing occupation-specific writing under no AI, passive AI copying, or active drafting-then-refining, plus a 270-person real-world survey. Passive copying reduced self-efficacy, ownership, and meaningfulness, with efficacy and meaningfulness losses persisting after returning to manual work, while active collaboration preserved connection similar to working alone.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0160

67Index score
535
PolicyStableModerate evidence · 1 source

Generative AI tools capable of producing wholly or partially synthetic CSAM increase risks of revictimization of known survivors, creation of synthetic material depicting children not previously abused, and facilitation of grooming, coercion, and sexual extortion.

As of its publication date 2026-04-06, this peer-reviewed primer examined generative AI systems able to produce wholly or partially synthetic child sexual abuse material and catalogued reported harms from technical, psychological, criminological, and law enforcement sources.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0157

67Index score
536
PolicyStableModerate evidence · 1 source

Rapid proliferation of AI-mediated digital afterlife technologies including chatbots trained on personal data, voice clones and posthumous avatars has created moral risks of posthumous simulation without operational governance constraints.

As of the June 2026 publication date, the authors describe a rapid proliferation of AI-mediated digital afterlife technologies and a growing ethical literature on their risks, without a matching operational framework. They propose a nine-dimensional taxonomy and a two-tier constraint model where consent, fidelity/disclosure, and purpose serve as threshold conditions for permissibility.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0153

67Index score

Recomputed live from the record · Oct 13, 2026, 6:21 PM