Privacy Washing: Detecting Internal Contradictions in Privacy Policies

URL SCAN: Privacy Washing: Detecting Internal Contradictions in Privacy Policies
FIRST LINE: # Computer Science > Computers and Society

The Dissection

This paper converts privacy-policy inconsistency into a measurable detection problem. It builds an LLM pipeline to extract statements, filter compatible pairs, judge contradictions, and classify recurring themes across 2015 and 2026 corpora. Its real function is not to establish deception. It is to demonstrate that privacy commitments and data practices routinely collide inside the same legal artifact, while carefully limiting the claim through caveats about human validation, model sensitivity, and incompatible filter configurations.

The strongest result is recurrence: similar contradiction categories appear across an eleven-year gap. The weakest result is prevalence. The system has detected model-confirmed textual tension, not verified corporate misconduct.

The Core Fallacy

The paper treats privacy washing primarily as a policy-composition defect. Under Discontinuity Thesis mechanics, the deeper issue is institutional: privacy policies are lagging legal interfaces attached to an economy whose value increasingly depends on continuous data extraction, automated inference, and machine coordination.

The contradiction is therefore not an aberration in the system. It is the system speaking with two voices: one designed to preserve legitimacy and another documenting the operational requirements of surveillance-dependent production. Better contradiction detection can expose the fracture. It cannot restore meaningful user control where the economic machinery is built around compulsory data surrender.

The paper also risks confusing linguistic contradiction with substantive contradiction. A majority vote among three LLMs creates procedural agreement, not truth. Without expert validation, its figures are lower bounds on model-recognized cases, not reliable measurements of actual privacy abuse.

Hidden Assumptions

• That privacy policies are intended to function as coherent, user-comprehensible commitments rather than liability shields and compliance documents.
• That contradictions can be cleanly identified from policy text without operational data, enforcement history, or implementation evidence.
• That model agreement is a meaningful proxy for correctness.
• That recurring categories reveal structural composition rather than repeated legal templates, shared drafting practices, or model-induced bias.
• That detecting contradictions materially improves user agency. The paper supplies no mechanism by which ordinary users can resist the practices disclosed.
• That the policy remains the relevant battlefield. In a data-intensive economy, the decisive power sits in infrastructure, identity systems, platforms, and automated decision systems—not in the prose presented to the user.

Social Function

Primary classification: partial truth and transition management, with a secondary function as ideological anesthetic.

It documents a real symptom and avoids the crudest claim of intentional deception. But its technical framing channels the problem into an auditable benchmark, where the remedy becomes better extraction, better judges, and better policy analysis. That is administratively useful and politically toothless. The institution can acknowledge contradiction, score it, and continue extracting data.

The LLM pipeline is itself part of the transition: automated systems auditing the textual defenses of automated data economies. The watchdog has already been mechanized; the underlying power relationship remains intact.

The Verdict

Privacy washing is real as a recurring contradiction pattern, but this paper measures the paperwork around the machine, not the machine’s power. Its prevalence estimates are methodologically provisional, and its central phenomenon is less a failure of corporate consistency than evidence that privacy language has become ceremonial cover for data-dependent production. Detection may improve diagnosis. It does not prevent the economic system from treating privacy as expendable lag infrastructure.