Ethical risk research on the interpretability of the AI algorithms

With the rapid development of artificial intelligence technology, AI algorithms are being applied in an increasing number of fields. While they make life more convenient for people, their “black box” nature has also raised concerns about algorithmic explainability. This paper explores the ethical risks associated with AI explainability, analyzes potential harms, and proposes strategies to promote the healthy development of AI algorithms. It also focuses on the need to enhance explainability and the current research status, discussing ethical issues such as bias, privacy, responsibility, and trust through case studies. The paper suggests improvement strategies based on LIME and SHAP models: combining the strengths of both models, developing efficient algorithms, using visualization tools, and leveraging automation technology to reduce ethical risks and uncertainties in human intervention.