Artificial intelligence (AI) has increasingly been applied to model entrepreneurial traits, offering new insights into constructs such as creativity, resilience, and risk-taking. However, these AI systems often inherit biases from datasets, feature selection, and algorithmic processes, which can misrepresent entrepreneurial potential and perpetuate inequities across gender, socioeconomic, and cultural groups. This chapter examines the conceptual foundations of bias and fairness in AI, explores techniques for detecting and mitigating bias, and highlights ethical considerations and policy implications for equitable deployment. It emphasizes the importance of inclusive data practices, fairness-aware algorithms, explainable AI, and participatory governance to foster trust and accountability in entrepreneurial ecosystems. Future directions focus on interdisciplinary approaches, culturally sensitive Modeling, and regulatory frameworks to ensure AI supports diversity, inclusivity, and responsible innovation in entrepreneurship research and practice.