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

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, such as 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 based on a single facial image with high accuracy in data 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 trained a Convolutional Neural Network (CNN) and evaluated its classification performance. While human experts (n = 650) and trained participants (n = 133) were unable to classify entrepreneurs with accuracy above chance levels (> 50%), the AI model achieved a classification accuracy of 79.51%. Several robustness tests show that this high level of accuracy is maintained under various conditions.