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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17909961 |
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Table of Contents:
- <p>As Large Language Models (LLMs) become ubiq-<br>uitous in decision-making, a critical sociotechnical<br>challenge has emerged: Automation Bias, where<br>users surrender agency to authoritative-sounding “Or-<br>acles.” We attribute this interaction failure to the<br>Certainty Bias Trap, a structural mechanism where<br>autoregressive models prematurely converge on a sin-<br>gle narrative.<br>To reshape this interaction, we introduce Struc-<br>tured Anti-Bias Prompting (SAP). Originating<br>from engineering root cause analysis in Sep 2025, SAP<br>transforms the LLM from an “Answer Engine” into a<br>“Mapmaker.” By mathematically enforcing diver-<br>sity via Multi-Perspective Validation, SAP acts<br>as a Cognitive Forcing Function. It prevents the<br>model from making the decision for the user, instead<br>providing the cognitive scaffolding necessary for the<br>user to act as the “Navigator.” This paper proposes<br>SAP not merely as a technical fix, but as a philosoph-<br>ical framework to restore Epistemic Agency in the<br>age of AI.</p>