From Oracle to Mapmaker: Protecting Human Agency via Structured Anti-Bias Prompting
Fuente:
Zenodo
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Recurso digital |
| Pubblicazione: |
Zenodo
2025
|
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866902007728570368 |
|---|---|
| author | Ishibashi, Ryuhei |
| author_facet | Ishibashi, Ryuhei |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17909961 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | From Oracle to Mapmaker: Protecting Human Agency via Structured Anti-Bias Prompting Ishibashi, Ryuhei <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> |
| title | From Oracle to Mapmaker: Protecting Human Agency via Structured Anti-Bias Prompting |
| url | https://doi.org/10.5281/zenodo.17909961 |