AI and Entrepreneurship: Facial Recognition Technology Detects Entrepreneurs, Outperforming Human Experts

2024-08-19

arXiv.org

AI Summary

This study investigates whether AI can infer entrepreneurial status solely from facial images, exposing a novel occupational privacy risk. We construct a paired dataset of 40,728 facial images—comprising entrepreneurs and non-entrepreneurs—curated from Crunchbase, and propose a contrastive learning–driven CNN model. We present the first empirical evidence that facial features enable stable prediction of entrepreneurial identity (79.51% accuracy), significantly outperforming human judgment (~50%) and conventional text- or behavior-based approaches. Rigorous robustness checks—including cross-dataset validation, demographic subgroup analysis, and adversarial perturbation tests—confirm result reliability. Our core contributions are threefold: (1) demonstrating that faces encode statistically discernible, AI-extractable signals correlated with occupational identity; (2) introducing the first visual reasoning framework specifically designed for entrepreneurial status recognition; and (3) providing critical empirical evidence and risk awareness to inform occupational privacy protection in the digital era.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• AI detects entrepreneurs from facial images
• Deep neural networks outperform human experts
• Privacy risks identified for entrepreneurs

Innovation

• Deep Neural Networks
• Facial Image Classification
• Contrastive Learning Approach