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REVIEW 4 major objections 4 minor 2 cited by

User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read All six major U.S. chatbot developers train on user chats by default, a 2025 policy review finds.

desk verdict Useful, carefully hedged policy survey with a real internal inconsistency: Table 1 codes Amazon 'Not Specified' for default chat training, yet the abstract and analysis say 'all six' train by default. read the letter →

arxiv 2509.05382 v1 pith:OHDVYJEX submitted 2025-09-05 cs.CY cs.AIcs.CR

Jennifer King , Kevin Klyman , Emily Capstick , Tiffany Saade , Victoria Hsieh This is my paper
classification cs.CYcs.AIcs.CR
keywords privacypolicieslargelanguagemodelschatbotstrainingdataconsentretentionCCPAchildren's
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Millions of people talk to chatbots, and those conversations can contain health details, biometric data, family information, and uploaded files. This paper asks whether the six largest U.S. frontier AI developers disclose in their privacy policies that they use those chats to train their models, and whether users can opt out. Working from the California Consumer Privacy Act's disclosure requirements, the authors coded 28 policy documents and linked notices as they stood in May 2025. They find that all six developers appear to use chat inputs for model training by default: Anthropic had just switched from opt-in to opt-out, Google and Meta offer no clear opt-out, and Amazon's written policies are silent, with training inferred from an interface notice. Three companies—Amazon, Meta, and OpenAI—appear to retain some chat data indefinitely, and four appear to include children's chat data in training. The paper's point is that privacy policies, the only comprehensive disclosure mechanism in U.S. law, are not telling consumers what happens to their chats.

What carries the argument

The analysis runs on a qualitative coding schema built from the CCPA's disclosure categories (categories of personal information collected, sources, business purposes, retention, sharing) plus LLM-specific categories such as default training, opt-out presence, human review, de-identification, and personalization. The schema was applied manually to 28 documents: each developer's main privacy policy, and every linked 'branch' sub-policy, FAQ, or interface notice needed to learn the chatbot's actual data practices. The key interpretive move is treating silence or 'not specified' as meaningful—so Amazon's default-training classification rests on a notice displayed in the Nova interface rather th

What would settle it

Create a fresh account at each developer with default settings, send a unique canary phrase in a chat, then submit a deletion request and probe publicly available versions of the models for memorization of that phrase; if the phrase appears, the company trained on default chats despite claiming not to, and if it never appears in any model, the default-training claim for that developer is weakened. For Amazon, checking whether the Nova interface notice appears without an account and whether the AWS privacy policy acknowledges training would directly test the paper's inference.

Watch

Extended reading notes

Core claim

The central claim is that, as of May 2025, every one of the six U.S. frontier chatbot developers—Amazon, Anthropic, Google, Meta, Microsoft, and OpenAI—uses consumers' chat inputs to train and improve its AI models by default, with no affirmative consent required. Only some offer opt-out mechanisms; Anthropic's move from opt-in to opt-out closed the last opt-in default among the six. The paper also claims that Amazon, Meta, and OpenAI appear to retain chat data indefinitely, and that OpenAI, Meta, and Amazon likely include chats from users aged 13-17, while Google trains on teen chats only with opt-in and Microsoft excludes authenticated minors. The authors stress that these conclusions come

Load-bearing premise

The findings depend on privacy-policy text and linked notices being a complete and truthful map of what companies actually do with chat data, and on the Anthropic policy change being read as a switch from opt-in to opt-out; the manuscript's footnote states the opposite direction, so if that footnote is right, the 'all six train by default' claim loses one of its six data points.

Editorial extensions

If this is right

  • Opt-out, not opt-in, is the industry default: consumers who want their chats excluded must find and use a settings control, and Google and Meta offer no clear route at all.
  • Because chats can contain sensitive personal data and uploaded files, default training means highly revealing material enters training corpora without affirmative consent.
  • Indefinite retention at Amazon, Meta, and OpenAI creates a long-lived target for breaches and a permanent dossier of a user's conversations.
  • Children aged 13-17 using OpenAI, Meta, and Amazon are likely included in training data by default, raising consent questions that existing U.S. law does not answer.
  • The two-tier arrangement that excludes enterprise customers from default training while applying it to consumers is a direct consequence of current defaults.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • My inference: if privacy policies remain the only public record, the May 2025 snapshot likely understates current practice, because policies change faster than papers publish; the authors' own note that Anthropic flipped its default shows the clock running.
  • My inference: the paper's method implies a cheap test for regulators—request the CCPA-mandated disclosures from each developer and compare them to what the policies say; discrepancies would expose which companies are training without documenting it.
  • My inference: the 'not specified' categories for Amazon suggest that companies may be choosing silence strategically, so an enforcement action requiring explicit statements about training defaults could shift industry practice without new legislation.
  • Manuscript note: the paper's footnote about Anthropic states the direction of the policy change as opt-out-to-opt-in, while the body text states opt-in-to-opt-out; the 'all six train by default' conclusion depends on the body-text reading, so a reader should verify which direction is accurate.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The manuscript analyzes the privacy policies, linked sub-policies, and interface notices of six U.S. frontier LLM developers (Amazon, Anthropic, Google, Meta, Microsoft, OpenAI) as of May 2025, using an inductive coding schema grounded in the CCPA and selected GDPR principles. It asks whether chatbot inputs and outputs are used for model training by default, what additional user data sources feed training, what opt-out mechanisms exist, how long chat data is retained, and how children's data are handled. The authors report that all six developers train on user chat data by default (after incorporating Anthropic's announced switch to opt-out), that some retain chat data indefinitely, and that four appear to train on children's chat data. They argue that developers' privacy policies are fragmented and incomplete, discuss normative implications of default training, indefinite retention, and children's data, and close with five policy recommendations.

Significance. If the findings are correct, the study is a timely and useful empirical contribution to AI privacy governance. It goes beyond general critiques of web scraping by focusing on the direct flow of consumer chat data into model training, and it compares six major firms with a consistent instrument. The paper's strengths include a transparent company-selection protocol, dual manual coding with cross-review, a clear external legal benchmark (CCPA), explicit limitation statements, and a decision not to use LLMs for coding. The policy recommendations are concrete and actionable. The main caveat is that the contribution's value depends on the coding being internally consistent and verifiable; in the current version, several table entries contradict the text, so the headline claims need strengthening before the empirical contribution can be fully credited.

major comments (4)
  1. [Abstract; §Analysis; Table 1] The headline claim that 'all six developers appear to employ their users' chat data to train and improve their models by default' (Abstract) is contradicted by Table 1, which codes Amazon's 'Chat input used for training by default' as 'Not Specified.' The §Analysis support is a Nova interface notice stating that interactions 'may be reviewed and retained' to 'provide, develop, and improve our services, including AI models.' This is permissive language about review and retention, not a disclosure of default training. Under the paper's own coding definition, ambiguous or undisclosed practices are 'Not Specified.' Since excluding Amazon reduces the finding to five of six, this classification is load-bearing and must be corrected or the claim narrowed.
  2. [Methodology; §Analysis; Discussion] The methodology fixes the policy snapshot at May 2025, and the §Analysis notes that Anthropic's privacy policy at that time required explicit opt-in for training. The Discussion then uses Anthropic's August/September 2025 announcement to assert that 'all six of the developers now train on their users' chat data by default.' The Abstract repeats 'all six' without a temporal qualifier. This mixes the analyzed snapshot with a later, announced state. Please state the date of the 'all six' claim explicitly and clearly separate the May 2025 snapshot from post-publication updates.
  3. [Children's data; Table 1] The children's data section says Amazon, Meta, and OpenAI allow users 13 and older and 'therefore likely train on it by default,' supporting the Abstract's 'four of six' count. Table 1, however, codes Amazon as 'No' for 'Allows accounts for children 13-18.' If Amazon does not allow minors, it should not be counted as training on children's data; if it does, Table 1 is wrong. This contradiction directly affects the reported number and must be resolved.
  4. [Policy selection protocol; Analysis] The empirical analysis is based on summaries of policy language, but the manuscript provides no direct quotations, URLs, or a shipped coding instrument/appendix. The crucial Amazon/Nova notice is paraphrased but not quoted, and references list 'Accessed: 2025-05-23' without links. For an empirical policy study, readers need to be able to check the coding. Please add a supplemental appendix with the codebook, relevant policy excerpts, URLs, and the coding decisions per company.
minor comments (4)
  1. [Footnote 1] Footnote 1 says Anthropic's policy update went 'from opt-out for model training to opt-in,' but the body text correctly says the change was from opt-in to opt-out. Reverse the wording.
  2. [Data retention] The text says 'Every chatbot developer that we analyzed appears to retain some chat data indefinitely,' which is inconsistent with Table 1 (only Amazon, Meta, and OpenAI are coded 'Yes' for indefinite retention) and with the later sentence 'Amazon, Meta, and OpenAI retain... indefinitely.' Clarify the definition of 'indefinitely' versus 'long-term' and align text with table.
  3. [Additional user-provided data sources; Table 2] The text says 'OpenAI, Google, and Microsoft also state that they may train on user-uploaded images,' but Table 2 codes Google as 'No' and Microsoft and OpenAI as 'Not Specified' for uploaded images. Align the text with Table 2 or correct the table.
  4. [References] Many references lack URLs despite 'Accessed' dates, and Google's two 2025 entries are both cited as (Google 2025), requiring 2025a/2025b disambiguation. Also fix the garbled 'Turow, Hennessy, and and 2018' reference and the malformed Staab et al. entry.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the paper's empirical findings are grounded in external policy documents and company notices, not in its own framework or prior self-citations.

full rationale

The paper's central claims—that frontier developers use chat data for training by default, retain data indefinitely, and lack clear opt-outs—are drawn from an inductive coding of 28 externally published privacy policies, sub-policies, FAQs, and interface notices as of May 2025. The CCPA is used as an external normative benchmark for evaluating comprehensiveness, not as a source from which the findings are derived. The authors' self-citations (e.g., Bommasani et al. 2023/2024/2025, King & Meinhardt 2024, Shen et al. 2025) appear only in background statements or in recommendations, and none is load-bearing for the empirical classification of any developer's practices. No equation, fitted parameter, or definitional identity is used to generate a predicted result from an input. The paper does contain an internal inconsistency—Table 1 codes Amazon's 'Chat input used for training by default' as 'Not Specified,' while the abstract and discussion count Amazon among 'all six' developers that train by default—and the Amazon classification rests on an interpretive reading of a Nova interface notice ('may be reviewed and retained' used to 'provide, develop, and improve our services, including AI models'). That is a validity and precision concern, not circularity: the claim is not equivalent to its input by construction, and the discrepancy does not involve the authors' framework substituting for external evidence. Similarly, the inference that Amazon, Meta, and OpenAI 'likely' train on children's chats because they allow 13+ accounts is an inference from policy facts, not a circular derivation. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This study does not introduce free parameters or invented entities. Its entire evidentiary base is qualitative: the text of privacy policies and sub-policies. The main epistemic commitments are that policy documents are truthful and comprehensive evidence of data practices, that absence of disclosure can be interpreted as absence of a user-visible opt-out, and that the CCPA provides the right normative lens for comparison.

assumptions (3)
  • domain assumption Privacy policies and linked sub-policies are comprehensive and accurate disclosures of companies' actual data collection and training practices.
    Introduced in the Introduction ('privacy policies remain the core resource for consumers seeking answers') and Methodology (treating policy text as evidence; coding 'Not Specified' as absence of a disclosed practice).
  • domain assumption Absence of a disclosed opt-out or exclusion implies that a practice, such as default training or training on children's chat data, is present.
    Used in the Analysis section when inferring 'all six train by default' from a mix of explicit statements and silence, and when inferring from account age policies that Amazon, Meta, and OpenAI 'likely' train on children's chats.
  • domain assumption The California Consumer Privacy Act is the appropriate normative framework for evaluating U.S. chatbot privacy policies.
    Methodology states the coding schema is 'grounded primarily' in CCPA because it is the most comprehensive U.S. law and all six companies serve California consumers; GDPR is mentioned but not the primary lens.

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Cite this review

Pith. "Pith review of User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies." pith.science (2026). https://pith.science/paper/OHDVYJEX

@misc{pith2026250905382,
  author       = {Pith},
  title        = {Pith review of: User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OHDVYJEX}},
  note         = {Machine review of arXiv:2509.05382}
}
read the original abstract

Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to each developer's relevant privacy policies to compare data collection and use practices across the six companies. We find that all six developers appear to employ their users' chat data to train and improve their models by default, and that some retain this data indefinitely. Developers may collect and train on personal information disclosed in chats, including sensitive information such as biometric and health data, as well as files uploaded by users. Four of the six companies we examined appear to include children's chat data for model training, as well as customer data from other products. On the whole, developers' privacy policies often lack essential information about their practices, highlighting the need for greater transparency and accountability. We address the implications of users' lack of consent for the use of their chat data for model training, data security issues arising from indefinite chat data retention, and training on children's chat data. We conclude by providing recommendations to policymakers and developers to address the data privacy challenges posed by LLM-powered chatbots.

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Forward citations

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Reference graph

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    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.