标题： 谁是更好的红娘？ 在高风险创业竞赛中的人类与算法法官分配 显示英文标题

标题： Who is a Better Matchmaker? Human vs. Algorithmic Judge Assignment in a High-Stakes Startup Competition

摘要： 随着对将人工智能（AI）应用于自动化和支持复杂决策任务的兴趣日益增长，然而，在需要语义理解和领域专业知识的背景下，算法与人类判断的比较仍不明确。我们在法官分配问题的背景下研究了这一问题，即将提交的项目与适当合格的法官进行匹配。具体来说，我们在哈佛校长创新挑战赛中解决了这个问题，这是该校最具影响力的创业竞赛，向学生和校友初创企业颁发超过50万美元的奖项。这代表了一个现实环境，其中高质量的法官分配至关重要。我们开发了一种基于人工智能的法官分配算法，混合词法-语义相似性集成（HLSE），并在比赛中部署了该算法。然后，我们使用评委对$309$法官-创业项目对的盲测匹配质量评分，将其性能与人工专家分配进行了评估。使用基于曼-惠特尼U统计量的检验，我们发现两种方法在分配质量上没有统计学上的显著差异（$AUC=0.48, p=0.40$）；平均而言，算法匹配被评为$3.90$，人工匹配为$3.94$，在5分制中，5表示优秀匹配。此外，在部署期间，之前需要一周时间的人工分配可以通过算法在几个小时内完成。这些结果表明，HLSE实现了与人类专家相当的匹配质量，同时提供了更大的可扩展性和效率，突显了人工智能驱动解决方案在高风险环境中支持和增强人类决策的潜力。 显示英文摘要

摘要： There is growing interest in applying artificial intelligence (AI) to automate and support complex decision-making tasks. However, it remains unclear how algorithms compare to human judgment in contexts requiring semantic understanding and domain expertise. We examine this in the context of the judge assignment problem, matching submissions to suitably qualified judges. Specifically, we tackled this problem at the Harvard President's Innovation Challenge, the university's premier venture competition awarding over \$500,000 to student and alumni startups. This represents a real-world environment where high-quality judge assignment is essential. We developed an AI-based judge-assignment algorithm, Hybrid Lexical-Semantic Similarity Ensemble (HLSE), and deployed it at the competition. We then evaluated its performance against human expert assignments using blinded match-quality scores from judges on $309$ judge-venture pairs. Using a Mann-Whitney U statistic based test, we found no statistically significant difference in assignment quality between the two approaches ($AUC=0.48, p=0.40$); on average, algorithmic matches are rated $3.90$ and manual matches $3.94$ on a 5-point scale, where 5 indicates an excellent match. Furthermore, manual assignments that previously required a full week could be automated in several hours by the algorithm during deployment. These results demonstrate that HLSE achieves human-expert-level matching quality while offering greater scalability and efficiency, underscoring the potential of AI-driven solutions to support and enhance human decision-making for judge assignment in high-stakes settings.