Ethical Dilemmas and Governance Paths of Algorithms in Fintech Innovation

Abstract

While improving service efficiency, fintech algorithm systems give rise to ethical dilemmas such as lack of transparency, ambiguous attribution of responsibility, and price discrimination, which further manifest as prediction inaccuracies caused by sample bias, liquidity suction from high-frequency trading, and the erosion of privacy boundaries through cross-domain data fusion. Based on an analysis of the generation mechanism and transmission logic of these dilemmas, this paper proposes three-tiered governance paths: restructuring the development process with embedded ethical constraints, an adaptive mechanism for a dynamic interpretability framework, and an algorithm audit and responsibility allocation model based on a reputation mechanism. These paths form a closed-loop system from origin to evaluation, providing a structured response framework for algorithmic ethical issues.