| Internet-Draft | CPoE Use Cases | May 2026 |
| Condrey | Expires 2 December 2026 | [Page] |
This document provides use case analysis and deployment considerations for the Cryptographic Proof of Effort (CPoE) framework defined in [CPoE-Protocol]. It describes how process-based authorship attestation addresses concrete needs across creative industries, academic integrity, journalism, legal and evidentiary standards, and content platforms. It also discusses common AI-assisted authoring workflows and how the Tool Receipt Protocol tracks human-to-machine effort attribution. This document is informational and does not define any protocol mechanisms; all normative requirements are specified in [CPoE-Protocol] and [CPoE-Appraisal].¶
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The Cryptographic Proof of Effort (CPoE) framework [CPoE-Protocol] provides a standardized, privacy-preserving mechanism for authors to produce cryptographic evidence of their creative process. Rather than analyzing finished output to infer how it was produced, CPoE captures the creation process itself: behavioral signals, temporal binding, and content evolution are recorded in tamper-evident Evidence Packets under the author's sole control.¶
This document describes the motivating use cases for CPoE. These use cases informed the design of the protocol and illustrate how process attestation addresses needs that neither content detection nor custody-based provenance can satisfy. Each use case maps to the RATS architecture roles (Attester, Verifier, Relying Party) defined in [CPoE-Protocol].¶
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here. This document is informational and uses these terms only when referencing normative requirements defined in [CPoE-Protocol] and [CPoE-Appraisal].¶
The provenance gap identified in [CPoE-Protocol] has concrete consequences across multiple domains. Where detection-based methods have been deployed as substitutes for process evidence, the results have been problematic: Liang et al. found that GPT detectors misclassified over 61% of essays by non-native English writers as AI-generated [Liang2023], and courts have begun ruling that sanctions based solely on AI detection scores lack valid evidentiary basis. The following use cases illustrate how process attestation addresses needs that neither content detection nor custody-based provenance can satisfy.¶
Importantly, CPoE does not assert that human-authored content is inherently superior to AI-generated content. The value of process attestation lies in transparency: enabling Relying Parties to make informed trust decisions appropriate to their context. In some contexts, AI-generated content is entirely acceptable or even preferred; in others, stakeholders require assurance about the nature and extent of human involvement. CPoE provides the evidentiary basis for these decisions without imposing a policy preference.¶
Professional guilds and labor organizations in the creative industries have established contractual frameworks governing AI use in content production. The Writers Guild of America's 2023 Minimum Basic Agreement provides that AI-generated material cannot be credited as "literary material" and requires studios to disclose when materials provided to writers incorporate AI-generated content. Similar provisions exist in SAG-AFTRA agreements for digital replicas and synthetic performers, and the Authors Guild has published model contract clauses addressing AI use.¶
These contractual obligations currently lack a standardized technical enforcement mechanism. A screenwriter's authoring application acts as the Attester, producing an Evidence Packet (.cpoe) that documents the writing process. When the writer uses AI tools for research or drafting, the Tool Receipt Protocol defined in [CPoE-Appraisal] records each AI contribution with a signed receipt binding the tool's output to the evidence chain. The studio or guild acts as the Relying Party, consuming the Attestation Result (.cwar) to verify compliance with contractual AI disclosure requirements. The Attestation Result includes the human-to-machine effort ratio, providing quantified attribution rather than a binary human-or-AI determination.¶
Educational institutions face a dilemma: post-hoc AI detectors produce probabilistic scores that cannot reliably distinguish AI-generated text from the writing of non-native speakers, students with certain disabilities, or those with concise writing styles [Liang2023]. Sanctions based on detector output alone have been overturned by courts as lacking valid evidentiary basis, with affected families incurring substantial legal costs. Institutions that rely on these tools expose themselves to litigation under due process and civil rights frameworks.¶
CPoE inverts this model. Rather than accusing students based on statistical analysis of their output, it enables students to voluntarily submit affirmative evidence of their authorship process. The student's writing environment acts as the Attester, producing an Evidence Packet over the course of the assignment. The institution's learning management system acts as the Relying Party, evaluating whether the submitted evidence is consistent with the claimed authorship timeline and effort. Participation is opt-in: CPoE serves as a provenance tool that rewards transparency, not a surveillance mechanism that presumes guilt.¶
This use case requires particular attention to privacy. Behavioral telemetry constitutes biometric data; the privacy protections defined in [CPoE-Protocol] (jitter quantization, data minimization, and Verifier data retention limits) are essential safeguards. Institutions deploying CPoE should establish clear data governance policies and ensure students retain control over their Evidence Packets.¶
Major news organizations including the Associated Press and BBC have adopted editorial policies requiring human oversight of AI-assisted content production, and several have co-founded initiatives to combat AI-generated disinformation. These policies require reporters to disclose AI tool use but lack a machine-verifiable mechanism for enforcement.¶
A reporter's authoring application acts as the Attester, producing Evidence Packets during article composition. The news organization's editorial system acts as the Verifier, appraising the evidence before publication. The resulting Attestation Result can be embedded in the article's metadata for downstream consumers (fact-checkers, aggregators, and readers) who act as secondary Relying Parties. When reporters use AI tools for research or drafting, Tool Receipts document each contribution, preserving an auditable record of the human editorial process that complements C2PA media provenance for any associated images or video.¶
CPoE Evidence Packets are designed to align with existing legal standards for digital evidence authentication. The U.S. Federal Rules of Evidence already provide a framework: Rule 901(b)(9) admits evidence about "a process or system, showing it produces an accurate result," and Rules 902(13) and 902(14) (amended 2017) establish that hash-authenticated electronic records and certified process outputs are self-authenticating. CPoE's hash-chained evidence structure, cryptographic signatures, and deterministic verification procedures map naturally to these evidentiary requirements.¶
Regulatory frameworks are also converging on mandatory AI content disclosure. The EU AI Act (Article 50) requires disclosure of AI-generated content published on matters of public interest, with enforcement beginning August 2026. These obligations create demand for a standardized technical mechanism that can produce verifiable, machine-readable attestation of how content was produced.¶
In this use case, a filer's authoring environment acts as the Attester, producing Evidence Packets bound to a hardware Secure Element (T3 or T4 tier as defined in [CPoE-Protocol]) for non-repudiation. The regulatory body or court acts as the Relying Party, consuming Attestation Results that include forgery cost estimates to quantify the economic assurance of the evidence.¶
Content platforms may distinguish between human-authored and AI-generated submissions for editorial placement, pricing, or labeling purposes. Authors act as Attesters, submitting Evidence Packets alongside their content. The platform's Verifier produces Attestation Results that inform trust decisions.¶
Absence Proofs as defined in [CPoE-Appraisal] enable the platform to identify submissions that lack process evidence, indicating unknown provenance rather than necessarily AI generation. The forgery cost bounds in the Attestation Result provide a quantitative basis for graduated trust: platforms can distinguish between content with strong process evidence, content with partial evidence, and content with no provenance at all, rather than relying on a binary authentic-or-not classification.¶
A common AI-assisted workflow involves the author typing prompts to an AI tool, receiving generated text, then editing and revising the output. In this scenario, the author's keystrokes for prompts and edits produce genuine behavioral signals, but the majority of the final content may be machine-generated.¶
CPoE addresses this through the Tool Receipt Protocol defined in [CPoE-Appraisal]: each AI tool contribution is recorded with a signed receipt binding the tool's output hash and character count to the checkpoint chain. The Attestation Result reports effort attribution (human-to-machine character ratio) derived from tool receipts, enabling Relying Parties to distinguish "author composed 5,000 words" from "author typed 200 words of prompts and edited 300 words of a 5,000-word AI-generated draft."¶
When tool receipts are absent, the behavioral signals attest only to the human interaction observed by the Attester; they do not prove that undisclosed AI tools were not used. Relying Parties SHOULD consider the absence of tool receipts when evaluating Attestation Results, as specified in [CPoE-Appraisal].¶
This document describes use cases and deployment considerations. It does not define protocol mechanisms or security-sensitive operations. The full security analysis for CPoE, including the threat model, forgery cost bounds, and adversarial analysis, is provided in [CPoE-Protocol] and [CPoE-Appraisal].¶
Deployers should note that the security properties available to each use case depend on the Attestation Tier selected. Tier 1 (CORE) provides software-only evidence with lower forgery cost bounds. Tiers 3 and 4 incorporate hardware Secure Elements that raise the forgery cost substantially and provide non-repudiation properties relevant to the legal and evidentiary use case (Section 2.4).¶
This document has no IANA actions.¶
The full privacy analysis for CPoE is provided in [CPoE-Protocol]. This section highlights privacy considerations specific to the use cases described in this document.¶
Behavioral telemetry collected by CPoE Attesters constitutes biometric data under multiple regulatory frameworks (e.g., GDPR, CCPA/CPRA, BIPA). Deployments in all use cases MUST comply with the privacy protections specified in [CPoE-Protocol], including jitter quantization of timing data, data minimization principles, and Verifier data retention limits.¶
The academic integrity use case (Section 2.2) requires particular care. Students may face implicit coercion to participate even when the system is nominally opt-in. Institutions deploying CPoE SHOULD establish clear data governance policies, obtain informed consent, and ensure that non-participation does not result in adverse inference or penalty.¶
The journalism use case (Section 2.3) involves potential identification of individual reporters through behavioral patterns. Organizations SHOULD configure Attesters to use the minimum evidence profile sufficient for their editorial requirements.¶
The use cases described in this document were informed by discussions with stakeholders across the creative industries, academic institutions, journalism organizations, and legal practitioners. The authors thank the contributors to the CPoE open specification process for their feedback on real-world deployment requirements.¶