Artificial Intelligence and Bias: Questions, Challenges, and Opportunities for Entrepreneurship

Artificial intelligence (AI) is increasingly embedded in entrepreneurial products and decisions, from customer targeting and credit scoring to recruitment and personalization. Alongside performance gains, AI introduces risks of systematic unfairness when models reproduce or amplify inequities in training data, model design choices, or deployment contexts. This research paper synthesizes recent research and governance frameworks to address three aims. To clarify how AI bias arises across the AI lifecycle, to map the practical challenges bias creates for entrepreneurs technical, organizational, reputational, and regulatory, and to identify venture opportunities emerging from responsible AI needs, such as bias auditing, explainable AI, and compliance by design tool chains. Drawing on standards and policy guidance and recent scholarship on responsible AI practice, the paper proposes an entrepreneurship oriented approach that treats fairness work as both risk reduction and market differentiation.