A Faceted Proposal for Transparent Attribution of AI-Assisted Text Production
- URL: http://arxiv.org/abs/2604.25346v1
- Date: Tue, 28 Apr 2026 08:03:16 GMT
- Title: A Faceted Proposal for Transparent Attribution of AI-Assisted Text Production
- Abstract summary: This paper proposes a faceted model for representing AI-assisted text production at the levels of documents, chapters, sections, and paragraphs.<n>The proposal introduces a core model based on Form, Generation, and Evaluation, and an extended model that adds Intent, Control, and Traceability.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial intelligence systems are increasingly integrated into writing processes, challenging traditional notions of authorship, responsibility, and intellectual contribution. Current disclosure practices usually indicate whether AI was used, but rarely explain how it was used, where it intervened, or how its output was reviewed. This paper proposes a faceted model for representing AI-assisted text production at the levels of documents, chapters, sections, and paragraphs. The proposal introduces a core model based on Form, Generation, and Evaluation, and an extended model that adds Intent, Control, and Traceability. The model is positioned as a minimal operational baseline with extensibility toward higher-fidelity representations. A worked example based on the production of this article demonstrates applicability.
Related papers
- Making AI-Assisted Grant Evaluation Auditable without Exposing the Model [0.0]
We propose a TEE-based architecture that helps reconcile requirements through remote attestation.<n>The architecture allows an external verifier to check which model, rubric, prompt template, and input representation were used.<n>We position the design relative to confidential AI inference, attestable AI audits, zero-knowledge machine learning, algorithmic accountability, and AI-assisted peer review.
arXiv Detail & Related papers (2026-04-28T04:10:04Z) - Inspectable AI for Science: A Research Object Approach to Generative AI Governance [35.790980610679384]
This paper introduces AI as a Research Object (AI-RO)<n>Instead of debating whether AI is an author or merely a tool, we propose treating AI interactions as structured, inspectable components of the research process.
arXiv Detail & Related papers (2026-04-13T10:13:20Z) - From Feature-Based Models to Generative AI: Validity Evidence for Constructed Response Scoring [0.0]
We compare the validity evidence needed in scoring systems using human ratings, feature-based natural language processing AI scoring engines, and generative AI.<n>The evidence needed in the generative AI context is more extensive than in the feature-based scoring context because of the lack of transparency and other unique concerns to generative AI such as consistency.
arXiv Detail & Related papers (2026-03-01T23:21:18Z) - Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Stud [2.659655189346942]
This paper presents a methodology for evaluating AIETs in language models.<n>We selected four AIETs: Model Cards, ALTAI, FactSheets, and Harms Modeling.<n>The evaluation considered the developers' perspective on the AIETs' use and quality in helping to identify ethical considerations about their model.
arXiv Detail & Related papers (2025-12-16T02:43:37Z) - General Scales Unlock AI Evaluation with Explanatory and Predictive Power [57.7995945974989]
benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems.<n>We introduce general scales for AI evaluation that can explain what common AI benchmarks really measure.<n>Our fully-automated methodology builds on 18 newly-crafted rubrics that place instance demands on general scales that do not saturate.
arXiv Detail & Related papers (2025-03-09T01:13:56Z) - Survey on Vision-Language-Action Models [0.2636873872510828]
This work does not represent original research, but highlights how AI can help automate literature reviews.<n>Future research will focus on developing a structured framework for AI-assisted literature reviews.
arXiv Detail & Related papers (2025-02-07T11:56:46Z) - CELA: Cost-Efficient Language Model Alignment for CTR Prediction [70.65910069412944]
Click-Through Rate (CTR) prediction holds a paramount position in recommender systems.<n>Recent efforts have sought to mitigate these challenges by integrating Pre-trained Language Models (PLMs)<n>We propose textbfCost-textbfEfficient textbfLanguage Model textbfAlignment (textbfCELA) for CTR prediction.
arXiv Detail & Related papers (2024-05-17T07:43:25Z) - Explainability in Process Outcome Prediction: Guidelines to Obtain
Interpretable and Faithful Models [77.34726150561087]
We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction.
This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
arXiv Detail & Related papers (2022-03-30T05:59:50Z) - Generative Counterfactuals for Neural Networks via Attribute-Informed
Perturbation [51.29486247405601]
We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP)
By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently.
Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
arXiv Detail & Related papers (2021-01-18T08:37:13Z) - Knowledge-Aware Procedural Text Understanding with Multi-Stage Training [110.93934567725826]
We focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process.
Two challenges, the difficulty of commonsense reasoning and data insufficiency, still remain unsolved.
We propose a novel KnOwledge-Aware proceduraL text understAnding (KOALA) model, which effectively leverages multiple forms of external knowledge.
arXiv Detail & Related papers (2020-09-28T10:28:40Z) - A Methodology for Creating AI FactSheets [67.65802440158753]
This paper describes a methodology for creating the form of AI documentation we call FactSheets.
Within each step of the methodology, we describe the issues to consider and the questions to explore.
This methodology will accelerate the broader adoption of transparent AI documentation.
arXiv Detail & Related papers (2020-06-24T15:08:59Z) - Few-Shot Learning for Opinion Summarization [117.70510762845338]
Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents.
In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text.
Our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.
arXiv Detail & Related papers (2020-04-30T15:37:38Z)
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.