Evaluating Trust and Recourse in Startup Success Prediction Models: A Comprehensive Review

Predicting startup success is both vital and challenging for entrepreneurs, investors, and policymakers. The combination of high failure rates and limited resources significantly affects investors’ ability to identify promising ventures early. This makes it difficult for early-stage ventures to secure funding, thus hampering innovation and novel ideas. Recently, there has been significant growth in the application of data-driven approaches such as supervised learning, ensemble methods, deep learning, and more recently, large language model fusion. Such methods utilize business information such as team size, funding stage, and industry in combination with digital signals obtained from social media, news, and online platforms.Despite these advancements, certain limitations and issues remain. The digital signals used for predictions are often prone to being manipulated or artificially inflated, creating risks for both models and potential investors. Various datasets that are commonly used suffer from unwanted bias and coverage issues, often overrepresenting larger startups. Existing predictive models simply act as opaque black boxes that just predict a success score without providing any actionable insights or recourse. As a result, startups are left with mere probabilities rather than clear, practical steps to improve their success likelihood.