Who Trusts AI with Their Emotions? Trust Formation and Sociodemographic Variation in LLM Use for Emotional Support
研究通过开发心理测量量表和结构方程模型，探讨了不同社会人口学特征用户对情感支持AI的信任形成机制及其差异。
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Impact of Image Resolution on Age Estimation with DeepFace and InsightFace
Input image resolution significantly affects the accuracy of automatic age estimation in face analysis systems, yet its impact remains poorly characterized across mainstream frameworks. Method: This study systematically investigates how resolution influences age estimation performance in DeepFace and InsightFace, evaluating both models on the IMDB-Clean dataset across seven resolutions (64×64 to 416×416) using Mean Absolute Error (MAE), Standard Deviation (SD), and Median Absolute Error (MedAE). Contribution/Results: We identify 224×224 as the optimal input resolution—deviations in either direction substantially increase estimation error. At this resolution, InsightFace achieves a MAE of 7.46 years, outperforming DeepFace (10.83 years) while exhibiting faster inference. This work provides the first systematic empirical validation of the non-monotonic relationship between input resolution and age estimation error in state-of-the-art face analysis frameworks, revealing critical inflection points in the resolution–error curve. The findings offer evidence-based guidance for optimizing image preprocessing pipelines in real-world deployment scenarios.
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The AI-Fraud Diamond: A Novel Lens for Auditing Algorithmic Deception
The technical opacity of AI systems enables novel, covert, and systemic fraud that eludes detection by traditional fraud theories. Method: This paper proposes the “AI Fraud Diamond Model,” extending the classic Fraud Triangle with a fourth element—“technical opacity”—and develops a taxonomy of AI fraud encompassing five categories, including data manipulation and model misuse. It further introduces a diagnostic auditing paradigm tailored for automated systems, shifting audit focus from outcome verification to systematic vulnerability identification. Contribution/Results: Grounded in qualitative interviews with auditors from major consulting firms and domain experts, the study validates the model’s explanatory power in uncovering auditors’ technical skill gaps, interdisciplinary collaboration barriers, and constraints on system access. The framework advances AI governance and intelligent auditing by offering a theoretically rigorous yet practically implementable analytical tool.
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Who Trusts AI with Their Emotions? Trust Formation and Sociodemographic Variation in LLM Use for Emotional Support
研究通过开发心理测量量表和结构方程模型，探讨了不同社会人口学特征用户对情感支持AI的信任形成机制及其差异。
0 citationsRead paper
Impact of Image Resolution on Age Estimation with DeepFace and InsightFace
Input image resolution significantly affects the accuracy of automatic age estimation in face analysis systems, yet its impact remains poorly characterized across mainstream frameworks. Method: This study systematically investigates how resolution influences age estimation performance in DeepFace and InsightFace, evaluating both models on the IMDB-Clean dataset across seven resolutions (64×64 to 416×416) using Mean Absolute Error (MAE), Standard Deviation (SD), and Median Absolute Error (MedAE). Contribution/Results: We identify 224×224 as the optimal input resolution—deviations in either direction substantially increase estimation error. At this resolution, InsightFace achieves a MAE of 7.46 years, outperforming DeepFace (10.83 years) while exhibiting faster inference. This work provides the first systematic empirical validation of the non-monotonic relationship between input resolution and age estimation error in state-of-the-art face analysis frameworks, revealing critical inflection points in the resolution–error curve. The findings offer evidence-based guidance for optimizing image preprocessing pipelines in real-world deployment scenarios.
0 citationsRead paper
The AI-Fraud Diamond: A Novel Lens for Auditing Algorithmic Deception
The technical opacity of AI systems enables novel, covert, and systemic fraud that eludes detection by traditional fraud theories. Method: This paper proposes the “AI Fraud Diamond Model,” extending the classic Fraud Triangle with a fourth element—“technical opacity”—and develops a taxonomy of AI fraud encompassing five categories, including data manipulation and model misuse. It further introduces a diagnostic auditing paradigm tailored for automated systems, shifting audit focus from outcome verification to systematic vulnerability identification. Contribution/Results: Grounded in qualitative interviews with auditors from major consulting firms and domain experts, the study validates the model’s explanatory power in uncovering auditors’ technical skill gaps, interdisciplinary collaboration barriers, and constraints on system access. The framework advances AI governance and intelligent auditing by offering a theoretically rigorous yet practically implementable analytical tool.
0 citationsRead paper