Largest public dataset of YC application videos and if they got accepted or not (twitter.com)

A new release bills itself as the largest public dataset of YC application videos paired with whether the company was accepted or rejected — a unique multimodal corpus linking short pitch videos to real-world outcomes. For AI/ML researchers this is significant because it provides labeled audiovisual data tied to a consequential decision, enabling work on outcome prediction, multimodal representation learning, and interpretability of human-presented signals (speech prosody, facial expressions, visual slides, and language content) in a high-stakes selection setting. Technically, the dataset opens doors for sequence and cross-modal models (video+audio+text), self‑supervised pretraining, and fine-grained behavior analysis (gesture, intonation, slide content) to understand which signals correlate with acceptance. It also raises major caveats: labels reflect complex, confounded human decisions (team background, traction, network effects), so naïve predictive models risk amplifying biases and making unethical inferences. Privacy, consent, and potential misuse (automated screening, profiling) are central concerns; any research should include robust fairness evaluation, causal analysis to separate spurious correlates, and privacy-preserving methods (differential privacy, restricted access). In short, the dataset is a powerful resource for multimodal ML and socio-technical research — but it demands careful, responsible handling and clear limitations on deployment.