On Algorithmic Ethics

The rapid advancement of artificial intelligence (AI) has endowed society with unprecedented decision-making capabilities while also raising profound ethical challenges. In the context of increasingly autonomous AI systems, balancing fairness, explainability, and accountability has become a frontier issue in technology ethics. Grounded in Kant's moral philosophy, this study introduced the universalization principle and ends-in-themselves principle into AI mechanism design and evaluation. By synthesizing existing scholarship and analyzing typical scenarios, an ethical constraint model was constructed for universalization testing, rights protection, and adaptive feedback. Using public datasets and real-world scenarios, the model was evaluated according to the criteria of fairness, explainability, and risk control. Findings show that internalizing Kantian ethics enhances system autonomy under moral norms and provides a robust pathway for algorithmic governance in complex social contexts. Integrating theory with practice, these innovative technical solutions offer guidance for optimizing future ethical algorithms.