AI仅凭人脸图像就能高精度识别创业者，隐私风险凸显。
AI, Entrepreneurs, and Privacy: Deep Learning Outperforms Humans in Detecting Entrepreneurs from Image Data
- 用对比学习训练CNN模型，通过成对人脸图像区分创业者与非创业者。
- 模型准确率达79.51%，远超人类专家的随机水平（>50%）。
- 适用于关注隐私安全、数据泄露风险的研究者与从业者。
职业结果如创业行为通常被视为个人隐私，个体应有自主决定是否披露的权利。随着人工智能从广泛可用的人类相关数据（如社交媒体）中推断私人信息的能力不断提升，有必要探究其能否准确提取此类信息。本研究证明，深度神经网络可基于来自Crunchbase（创业数据权威来源）的面部图像，以高准确率识别创业者。我们使用包含40,728名个体（含创业者与非创业者）的面部图像数据集，采用对比学习方法训练卷积神经网络（CNN），每对样本包含一名创业者与一名非创业者。尽管人类专家（n=650）和受训参与者（n=133）的分类准确率均未超过随机水平（>50%），我们的AI模型实现了79.51%的分类准确率。多项稳健性测试表明该高准确率在多种条件下依然保持。结果表明创业者面临显著隐私风险。
原文摘要 · Abstract (English)
Occupational outcomes like entrepreneurship are generally considered personal information that individuals should have the autonomy to disclose. With the advancing capability of artificial intelligence (AI) to infer private details from widely available human-centric data (e.g., social media), it is crucial to investigate whether AI can accurately extract private occupational information from such data. In this study, we demonstrate that deep neural networks can classify individuals as entrepreneurs with high accuracy based on facial images sourced from Crunchbase, a premier source for entrepreneurship data. Utilizing a dataset comprising facial images of 40,728 individuals, including both entrepreneurs and non-entrepreneurs, we train a Convolutional Neural Network (CNN) using a contrastive learning approach based on pairs of facial images (one entrepreneur and one non-entrepreneur per pair). While human experts (n=650) and trained participants (n=133) were unable to classify entrepreneurs with accuracy above chance levels (>50%), our AI model achieved a classification accuracy of 79.51%. Several robustness tests indicate that this high level of accuracy is maintained under various conditions. These results indicate privacy risks for entrepreneurs.
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