Predicting startup success using Machine Learning has become a rapidly growing field of research. However, existing literature relies almost exclusively on traditional metrics, such as funding, team size, and founder networks. This paper
addresses a critical gap: current predictive models treat Artificial Intelligence (AI) based startups as conventional software companies, overlooking their unique technical, ethical, and legal challenges.
With strict rules like the EU AI Act now in place, AI ethics and data privacy are no longer just 'nice-to-have' theoretical concepts—they are critical for a startup's survival and its ability to attract investors. The core novelty of this study is that it takes these abstract ethical and regulatory demands and turns them into actual, measurable data points for Machine Learning models. By looking at a startup's practical digital footprint—such as their GitHub repositories and official website communication—we build real-world indicators that show how serious a company truly is about algorithmic fairness, privacy, and transparency. Using a curated pilot sample of approximately 100 AI-based startups, the findings of this research introduce an initial framework for evaluating entrepreneurial risk. By bridging the gap between technological innovation and regulatory compliance, this study provides investors and founders with an early, data driven decision-support signal. Ultimately, it moves beyond the "regulatory blindness" of past models, providing preliminary evidence that long-term startup viability in the modern tech ecosystem is tight together with responsible AI governance.