Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
A new research paper introduces PEEL, a framework designed to enhance epistemic accountability in AI-assisted research. PEEL combines traditional text analysis tools like Voyant Tools with LLM interpretations from models such as Claude, all grounded in Peircean semiotics and abductive reasoning. The framework aims to identify systematic distortions in AI-generated research summaries, highlighting the need for deterministic measurement alongside AI tools to ensure fidelity and design in epistemic authority. AI
IMPACT Introduces a method to mitigate epistemic risks in AI-assisted research, promoting more reliable AI outputs.