AI and DeFi Integration: Algorithmic Bias, Black-Box Opacity, and Regulatory Challenges

Abstract

The integration of AI and DeFi drives financial innovation with applications like smart contract security and automated trading but raises critical challenges. This paper examines algorithmic bias, black-box opacity, and regulatory lags via peer-reviewed literature, industry/institutional reports, and cases (e.g., Apple Card, COMPAS). It analyzes how biased training data perpetuates discrimination in financial applications, while opaque AI models hinder bias detection and auditability, amplifying market volatility and systemic risks. EU, U.S., and Chinese regulatory frameworks face gaps in addressing technical complexity and cross-border fragmentation. Results show AI-DeFi integration exacerbates inequity and erodes trust and there is limited research focused on AI-DeFi specified risks and mitigating mechanism. Conclusions stress interdisciplinary governance, global regulatory coordination, and transparent AI architectures to mitigate risks, ensuring equitable/secure financial ecosystems to support regulatory management and sustainable development.