Disinformation as weaponised ignorance: A hybrid teleological–epistemic framework | ||
| Journal of Cyberspace Studies | ||
| دوره 10، شماره 2، مهر 2026، صفحه 493-512 اصل مقاله (475.58 K) | ||
| نوع مقاله: Original article | ||
| شناسه دیجیتال (DOI): 10.22059/jcss.2025.403904.1190 | ||
| نویسندگان | ||
| Ramin Rambod؛ Hadi Samadi* ؛ Shahla Eslami | ||
| Department of Philosophy, SR.C. Islamic Azad University, Tehran, Iran. | ||
| چکیده | ||
| Background: Contemporary debates on disinformation are dominated by two influential approaches: Fallis’s functional (teleological) model and Simion’s purely epistemic Disinformation as Ignorance-Generating Content (DIGC) model. Their respective strengths and weaknesses become salient in real-world settings such as the Backfire Effect, the spread of bullshit, and disputes about the epistemic status of Large Language Models (LLMs). Aims: We aim to (i) critically evaluate the explanatory and classificatory utility of the functional and purely epistemic models across these scenarios, (ii) diagnose key failure modes (especially DIGC’s over-generation and the functional model’s difficulties with non-intentional sources), and (iii) propose a more extensionally adequate framework. Methodology: We conduct a comparative conceptual analysis of both models and test their classifications against several cases. In particular, we use empirical findings on the Backfire Effect to examine whether a purely consequence-based criterion misclassifies accurate, well-intentioned scientific information. We also incorporate Frankfurt’s distinction between lying and bullshit to refine how epistemic malice is characterised. Findings: Fallis’s functional model captures complex forms of disinformation (including true and adaptive disinformation) by tying disinformation to a misleading function, but it struggles to classify outputs from non-intentional sources such as autonomous AI. DIGC broadens coverage by removing intentionality and focusing on dispositions to increase ignorance, yet this purely epistemic stance yields an Over-generation Problem: under Backfire conditions, it can wrongly classify accurate and well-intentioned scientific communication as disinformation. To address these limitations, we propose a hybrid teleological framework, Functional-Contextual Disinformation (FC-DIGC), which combines DIGC’s consequence criterion with a teleological constraint requiring a misleading function. This synthesis better separates malicious deception (disinformation) from unintended epistemic harm (contextually harmful misinformation) and helps clarify how LLM outputs should be categorised. Conclusion: A hybrid teleological approach improves extensional adequacy by preventing over-generation while retaining coverage for non-intentional systems. FC-DIGC provides a principled way to distinguish disinformation from contextually harmful misinformation and, by integrating the lying–bullshit contrast, captures a broader spectrum of epistemically motivated malice relevant to contemporary information environments, including AI-mediated communication. | ||
| کلیدواژهها | ||
| bullshit؛ disinformation؛ epistemic authority؛ ignorance-generating content؛ intentional misleading؛ knowledge-first epistemology؛ LLMs | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 474 تعداد دریافت فایل اصل مقاله: 352 |
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