In recent years, the real-world implementation of artificial intelligence (AI) technology in the financial sector has continued to deepen. With more accurate prediction models, higher operational efficiency, and personalized services, this technology has reshaped the generation model of investment decision-making. However, the large-scale deployment of AI has also given rise to two core categories of risks. First, AI systems inherently face the problems of algorithmic opacity and implicit systemic bias. Second, preexisting cognitive biases among retail investors—such as overconfidence and the anchoring effect—are further amplified by AI-powered digital financial platforms. This study draws on two core theoretical frameworks, behavioral finance and AI ethics, to focus on the coexistence logic of human behavioral biases and algorithmic biases, and develops an analytical tool that integrates both types of bias. At the same time, the study identifies conceptual gaps in the existing traditional definition of financial literacy, puts forward the concept of algorithmic literacy as a component of digital literacy, and clarifies the core competencies investors must have in AI-driven investment scenarios: the ability to critically evaluate AI-generated investment advice, and to offset their own behavioral biases through active human engagement. This study argues that AI development must implement robust ethical principles encompassing transparency, accountability, and digital trust, to optimize governance systems that promote the responsible use of AI. By moving beyond the narrow framework of traditional financial literacy, this research provides support for aligning AI innovation with consumers’ financial well-being. The study’s findings are usable for financial education practitioners, policymakers, and financial institutions, to advance investor protection, and ensure that technological research and development aligns with ethically sound financial decision-making.