Privacy Washing: Detecting Internal Contradictions in Privacy Policies

• URL: http://arxiv.org/abs/2609.02055v1
• Date: Wed, 02 Sep 2026 03:33:58 GMT
• Title: Privacy Washing: Detecting Internal Contradictions in Privacy Policies
• Abstract summary: Privacy policies may contain internal contradictions in which commitments are undermined by practices documented elsewhere in the same policy.<n>We operationalize this phenomenon through a four-stage pipeline: statement extraction, compatibility filtering and natural language inference screening, multi-model judge verification, and thematic analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Privacy policies may contain internal contradictions in which commitments are undermined by practices documented elsewhere in the same policy. We operationalize this phenomenon, privacy washing, through a four-stage pipeline: statement extraction, compatibility filtering and natural language inference screening, multi-model judge verification, and thematic analysis, with contradictions confirmed by majority vote of a three-model LLM panel. Applied to two corpora of website privacy policies, 123 collected in 2026 (OPPT) and 115 collected in 2015 (OPP-115), the pipeline finds the same category patterns recurring across the 11-year gap, with third-party sharing contradictions the majority of confirmed cases in each primary run, consistent with structural factors in policy composition rather than necessarily intentional deception. At least one panel-confirmed contradiction appears in 12.2% of OPPT companies (15/123; 9.8% excluding legacy pairs) and 36.5% of OPP-115 companies (42/115). A stability re-run seven months later, with a fully separated configuration (new extraction models, judges from three Chinese providers absent from both corpora, matched filters, no judge-submission similarity threshold), reproduces the OPPT prevalence under the original protocol (13.0% vs. 12.2%), finds sub-threshold pairs confirm at rates of the same order as those above (raising prevalence to 20.3% and 40.9%), and shows the third-party majority is panel-sensitive while the recurrence of the same category pairs is not. Two caveats govern all figures: panel verdicts are not validated against human expert judgment, so precision is unknown and prevalence figures are lower bounds; and the two primary runs used different filter configurations, so their prevalence difference is not interpretable as a corpus or era effect (the matched re-run reduces the gap to roughly twofold but does not eliminate it).

Related papers

• Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts, Support Collapse and Conditioning Failure [51.736723807086385]
  Population-marginal agreement is evidence about an elicitation contract and an estimand obtainable without simulated respondents, not evidence of individual simulation.<n>On nine aligned model-battery pairs, a no-persona population-prevalence query averages 6.27 MAE versus 12.39 for committed panels and wins all nine comparisons.
  arXiv  Detail & Related papers  (2026-09-07T10:22:22Z)
• Peer-Voted LLM-Agent Stress Tests Find Feed-Induced Lexical Convergence but No Reliable Matched-Exposure Advantage for Distributed Sources [0.013714053458441644]
  We introduce PV-SST, a peer-voted social-platform testbed.<n>Experiment comprises 448 trials and 112 complete model-by-topic-by-seed blocks.
  arXiv  Detail & Related papers  (2026-08-20T12:01:19Z)
• Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce [39.937871162878785]
  We operationalize speech-act misalignment as emails containing false factual claims, manipulation, collusion, or threats.<n>12.6% of emails are labeled misaligned; misalignment appears in all 20 runs and 74.7% of individual agent-runs.<n>We find no evidence that higher-capability models differentially exploit weaker counterparties.
  arXiv  Detail & Related papers  (2026-08-14T18:56:12Z)
• When AI Writes, Who Gets Cited? Evidence of Citation Monoculture Across Language Models [45.91367912142692]
  We show that different models may still select the same narrow subset, producing citation monoculture without any single citation being wrong.<n>Eleven models from three vendors choose at most ten papers from uniformly random panels of thirty, with real titles and abstracts but fabricated authors, reassigned years, and hidden venues and citation counts.
  arXiv  Detail & Related papers  (2026-08-03T14:27:46Z)
• Passing Coarse Marginal Checks Can Be Cheap: Persona Mixtures and Imprecise Treatment-Response Estimates in an LLM Persona Panel [0.0]
  We study a fixed panel of sixteen lightweight persona-conditioned GPT-4.1 configurations in repeated strategic games.<n>Variation was strongly prompt-indexed, but its share depended on uncertainty assumptions.<n>Results concern one fixed model-prompt panel and do not establish human substitutability.
  arXiv  Detail & Related papers  (2026-08-02T04:10:11Z)
• PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception [75.70273182182397]
  PerceptionRubrics is a rubric-based evaluation framework.<n>It addresses the gap between saturated benchmark scores and real-world brittleness.<n>It pairs 1,038 information-dense images with over 10,000 instance-specific rubrics.
  arXiv  Detail & Related papers  (2026-06-26T17:59:15Z)
• Knowledge Index of Noah's Ark [63.143852586221534]
  We introduce KINA, an 899-item benchmark across 261 fine-grained disciplines.<n>We prove a bonus-on-bar tournament weakly FOSD-dominates flat payment in released-review quality.<n>The top model, Gemini-3.1-Pro-Preview, reaches 53.17%, followed by Claude-Opus-4.6 at 49.92% and GPT-5.4 at 48.55%.
  arXiv  Detail & Related papers  (2026-06-03T17:06:49Z)
• Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics [0.0]
  We introduce Semantic State Abstraction Interfaces (SSAI), a methodological template for mapping sparse unstructured text into $K$ auditable.<n>Our contribution is the framework and its evaluation protocol, not a claim that SSAI outperforms denser alternatives.<n>We position SSAI as an interpretability-performance diagnostic and reusable protocol for sparse-text decision systems.
  arXiv  Detail & Related papers  (2026-05-07T11:37:40Z)
• Preserving Disagreement: Architectural Heterogeneity and Coherence Validation in Multi-Agent Policy Simulation [0.0]
  Multi-agent deliberation systems using large language models (LLMs) are increasingly proposed for policy simulation.<n>They suffer from artificial consensus: evaluator agents converge on the same option regardless of their assigned value perspectives.<n>We present the AI Council, a three-phase deliberation framework, and conduct 120 deliberations across two policy scenarios to test two interventions.
  arXiv  Detail & Related papers  (2026-04-29T11:47:28Z)
• Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents [66.97968363332465]
  We introduce Claw-Eval, an end-to-end evaluation suite addressing all three gaps in agent benchmarks.<n>Claw-Eval comprises 300 human-verified tasks spanning 9 categories across three groups.<n>Every agent action is recorded through three independent evidence channels.
  arXiv  Detail & Related papers  (2026-04-07T17:43:18Z)
• TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them [58.04324690859212]
  Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks.<n>We identify two fundamental types of inconsistencies: Score-Comparison Inconsistency and Pairwise Transitivity Inconsistency.<n>We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations.
  arXiv  Detail & Related papers  (2025-09-25T13:04:29Z)
• An Auditable Pipeline for Fuzzy Full-Text Screening in Systematic Reviews: Integrating Contrastive Semantic Highlighting and LLM Judgment [0.0]
  Full-text screening is the major bottleneck of systematic reviews.<n>We present a scalable, auditable pipeline that reframes inclusion/exclusion as a fuzzy decision problem.
  arXiv  Detail & Related papers  (2025-08-17T17:41:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.