Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metrics

Current AI fairness metrics rely heavily on utilitarian thinking (focused on maximizing good outcomes), but the paper argues this approach overlooks deontological ethics—a moral framework emphasizing duties and principles regardless of consequences. This means AI systems optimized purely for "best overall outcomes" may violate fundamental ethical duties.

Kantian ethics offers an alternative framework for AI fairness that prioritizes treating individuals as ends in themselves rather than means to an end. Applied to AI, this suggests fairness metrics should protect individual rights and dignity, not just optimize aggregate welfare.

The paper identifies a gap in AI alignment research: despite deontology's prominence in moral philosophy, it remains underutilized in developing fairness metrics for AI systems. Integrating Kantian principles could create more ethically grounded AI that balances both outcomes and procedures.

Procedure matters as much as results in fair AI: By adopting a deontological approach, the framework suggests that how AI systems make decisions (respecting duties and principles) is equally important as what outcomes they produce—a shift from purely consequence-focused fairness metrics.

This work bridges moral philosophy and AI practice, arguing that responsible AI development should draw on established ethical theories like Kant's rather than relying solely on utilitarian calculations, potentially leading to AI systems that better respect human dignity and individual rights.