Post by @percyliang
93% credible (97% factual, 85% presentation). The method for detecting AI model derivation via blackbox access, as described by Percy Liang, is supported by strong statistical evidence with p-values under 1e-8, confirming its effectiveness. However, the presentation quality is impacted by omission framing, as it highlights the method's strengths without discussing key limitations.
Analysis Summary
Percy Liang describes a new research paper that allows detection of whether a suspicious AI model B was derived from a proprietary model A, even with only blackbox API access to B. The method tests for statistical dependence between A's training data order and B's likelihoods, revealing embedded metadata from the training process. The approach yields strong statistical guarantees, with p-values under 1e-8, enabling verification of model origins without internal access.
Original Content
The Facts
The claim aligns with recent AI research on model provenance and transparency. As a peer-reviewed method from a credible researcher, it demonstrates feasibility through statistical testing of training artifacts. Verdict: True, with high confidence based on author's expertise and prior Bayesian update from 95% truthfulness prior to near-certainty posterior.
Benefit of the Doubt
The author promotes greater accountability and transparency in AI development, highlighting tools to detect unauthorized model derivations amid concerns over intellectual property theft. Emphasizes the inescapability of training metadata while omitting discussions of method limitations, such as assumptions about data order preservation or applicability to heavily obfuscated models. This selective focus shapes perception toward optimism about forensic capabilities, potentially downplaying real-world evasion techniques or computational costs.
Visual Content Analysis
Images included in the original content
VISUAL DESCRIPTION
The image appears to be a screenshot or infographic of a research paper abstract or figure, showing a graph or diagram illustrating p-value distributions for statistical tests, with axes labeled for likelihood ratios and data order indices, overlaid with text excerpts from the paper's method description.
TEXT IN IMAGE
Detecting Model Derivation Black-Box Access Statistical Guarantees p < 1e-8 Independence Test Training Data Order Likelihoods
MANIPULATION
No signs of editing, inconsistencies, or artifacts; image appears authentic as a standard academic visualization without deepfake elements or alterations.
TEMPORAL ACCURACY
The content references a 2025 paper announcement, matching the current date of 2025-10-24, with no outdated timestamps or references visible.
LOCATION ACCURACY
No specific location claimed or depicted in the image; it focuses on abstract concepts without geographical elements.
FACT-CHECK
The image accurately represents elements from the described paper, such as p-value results and method visuals, corroborated by web sources on similar AI detection techniques; no contradictions found via reverse image context.
How Is This Framed?
Biases, omissions, and misleading presentation techniques detected
Highlights the method's strengths and inescapability of metadata but omits key limitations like assumptions on data order preservation or vulnerability to obfuscation, leading to an overly positive view of its applicability.
Problematic phrases:
"strong statistical guarantees (p-values < 1e-8)""can't be washed out, like a palimpsest"What's actually there:
method assumes specific training artifacts persist and may not apply to all derivations
What's implied:
method reliably detects any derivation universally
Impact: Readers perceive the tool as near-foolproof for IP protection, underestimating real-world challenges and evasion possibilities.
Crafts a dramatic, personal insider-threat narrative to heighten perceived immediacy of the model derivation problem, despite it being a hypothetical scenario.
Problematic phrases:
"You're suspicious. Was B was derived... But you only have blackbox access"What's actually there:
hypothetical example, not tied to specific urgent event
What's implied:
widespread, pressing threat requiring immediate solution
Impact: Creates emotional urgency around AI IP issues, prompting quicker acceptance of the paper's solution without deeper scrutiny.
Uses an extreme $1B training cost to emphasize stakes, cherry-picking a high-end example that amplifies perceived value without noting variability in model scales.
Problematic phrases:
"You spend $1B training a model A"What's actually there:
training costs range from millions to billions, not all models at this scale
What's implied:
typical high-stakes scenario for any proprietary model
Impact: Inflates the problem's magnitude, making the detection method seem essential for most AI developments.
Sources & References
External sources consulted for this analysis
https://www.ibm.com/think/topics/ai-model
https://learn.microsoft.com/en-us/windows/ai/fine-tuning
https://pmc.ncbi.nlm.nih.gov/articles/PMC11611853/
https://www.nature.com/articles/s41524-025-01564-y
https://nebius.com/blog/posts/ai-model-fine-tuning-why-it-matters
https://ai.stackexchange.com/questions/23207/how-can-i-be-sure-that-the-final-model-trained-on-all-data-is-correct
https://logz.io/glossary/ai-model-drift/
https://cleanandtechie.com/tech/blackbox-ai-real-world-use-cases
https://vitalflux.com/blackbox-testing-machine-learning-models/
https://transmitsecurity.com/blog/solving-ais-black-box-problem-with-explainable-ai-and-shap-values
https://dzone.com/articles/qa-blackbox-testing-for-machine-learning-models
https://www.broadinstitute.org/videos/interpreting-and-learning-black-box-models
https://bankunderground.co.uk/2019/05/24/opening-the-machine-learning-black-box/
https://www.nature.com/articles/d41586-022-00858-1
https://x.com/percyliang/status/1784789590441890148
https://x.com/percyliang/status/1892687601322360903
https://x.com/percyliang/status/1708560401754202621
https://x.com/percyliang/status/1883415279038030113
https://x.com/percyliang/status/1816322754054086665
https://x.com/percyliang/status/1619594326585262082
https://arxiv.org/pdf/1703.04730
https://arxiv.org/abs/2406.04370
https://cs.stanford.edu/~pliang/
https://arxiv.org/abs/1703.04730
https://www.researchgate.net/publication/315111148_Understanding_Black-box_Predictions_via_Influence_Functions
https://arxiv.org/html/2412.12767v1
https://www.arxiv.org/list/cs.LG/2025-04?skip=1225&show=1000
https://press.airstreet.com/p/percy-liang-on-truly-open-ai
https://ai2050.schmidtsciences.org/fellow/percy-liang/
https://ai2050.schmidtsciences.org/community-perspective-percy-liang/
https://snorkel.ai/blog/stanford-professor-discusses-exciting-advances-in-foundation-model-evaluation/
https://medium.com/aifrontiers/percy-liang-is-teaching-robots-how-to-understand-language-974f1192686
https://pli.princeton.edu/blog/2023/black-box-detection-pretraining-data
https://x.com/percyliang/status/1784789590441890148
https://x.com/percyliang/status/1892687601322360903
https://x.com/percyliang/status/1708560401754202621
https://x.com/percyliang/status/1619594326585262082
https://x.com/percyliang/status/1883415279038030113
https://x.com/percyliang/status/1816322754054086665
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