感知与实际AI深度伪造：以苏丹为例
Perceived and Actual AI Deepfakes: The Case of Sudan
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中文总结 AI 辅助
针对苏丹AI深度伪造风险，研究发现需求侧脆弱性（如动机性推理和情境依赖）比内容数量更关键，提出短期应侧重预辟谣、AI素养等跨模态干预以增强公众抵御力。
中文摘要 AI 辅助
苏丹的AI深度伪造风险目前似乎更多由需求侧脆弱性驱动，而非AI深度伪造内容的数量。尽管苏丹信息流中AI生成的虚假信息仍然有限，但感知（被指控的）AI深度伪造和普遍的AI怀疑论使局势恶化，并加剧了多媒体内容中的普遍不确定性。在分析案例中，我们发现主要立场是不信任，这种不信任更多由动机性推理和情境依赖驱动，而非持久的AI检测技能。在实践中，对AI深度伪造的拒绝或接受往往反映信念驱动的判断，而非技术评估。这些薄弱防御使信息环境易受未来复杂AI深度伪造活动的影响，并存在使对真相漠视正常化的风险。短期干预应优先考虑跨模态的需求侧干预，如预辟谣、AI素养教育、规模化事实核查和举报热线，以增强公众对所有形式虚假信息的抵御能力。更具体的AI技术性和供给侧干预可在机构能力和技术条件允许时逐步发展。
英文摘要
Sudan's AI deepfake risk currently appears driven more by demand-side vulnerabilities than the volume of AI deepfake content. While AI-generated disinformation in Sudanese feeds remains limited, perceived (alleged) AI deepfakes and general AI skepticism worsen the situation and contribute to general uncertainty in multimedia content. Within the analyzed cases, we found that the prominent stance was distrust driven more by motivated reasoning and contextual reliance than by durable AI detection skills. In practice, rejection or acceptance of AI deepfakes often reflects belief-driven judgment more than technical assessment. These weak defenses leave the information environment vulnerable to future sophisticated AI deepfake campaigns and risk normalizing truth indifference. Short-term interventions should prioritize modality-agnostic demand-side interventions such as pre-bunking, AI literacy, scaled fact-checks, and tiplines that strengthen public resilience across all forms of disinformation. More technical AI- specific and supply-side interventions can be developed as institutional capacity and technology allow.
