Overview
Field: Machine Learning Authors: Clarisse de Souza, Gabriel Barbosa, Simone Diniz Junqueira Barbosa Published: 2025-06-01 arXiv: 2506.00635
Abstract
Large language models are reshaping research practice while quietly eroding researchers' epistemic accountability. This commentary introduces PEEL — Protocols for Epistemically Engaged Literacy in AI, a working scaffolding that combines deterministic distant reading via Voyant Tools with LLM interpretation via Claude, grounded in Peircean semiotics and abductive reasoning.
Applied to AI-generated condensations of three source texts, PEEL reveals systematic distortions in quantity, term frequency, and epistemic voice that are invisible without non-AI measurement — and yields three design implications:
1. Deterministic instruments must accompany AI tools. Non-AI measurement is essential to make AI-induced distortions visible. 2. Fluency is not fidelity. A polished LLM summary may systematically diverge from its source. 3. Epistemic authority must be designed in, not assumed. Research workflows should build accountability into the process itself.
Links
- Paper: https://arxiv.org/abs/2506.00635