Adaptive Kantian AI: a constraint-based approach to the categorical imperative in multi-agent systems

Abstract

Purpose This paper aims to address a core limitation in computational Kantian ethics: the tension between deontological rigidity and the demands of decision-making in complex, real-world environments. It proposes an adaptive extension of the categorical imperative that preserves its non-consequentialist foundations while enabling context-sensitive application. Design/methodology/approach The authors develop a constraint-based computational framework in which moral values are treated as conditions of admissibility rather than optimization targets. The model introduces dynamic tolerance and impact thresholds to enable bounded flexibility. It is empirically evaluated on the ETHICS data set through comparison with baseline Kantian and reinforcement learning from human feedback -based models. Findings The adaptive formulation improves behavioral alignment across diverse scenarios while maintaining strong deontological consistency. It systematically rejects compensatory trade-offs typical of reward-optimizing systems, while allowing conditionally admissible decisions in cases of conflicting duties under strict structural constraints. Research limitations/implications The framework depends on calibrated parameters and predefined value structures, which may introduce subjectivity and limit generalizability. Scalability and long-term stability in multi-agent settings remain open challenges. Practical implications The model supports auditable, constraint-aligned decision-making in high-risk domains requiring transparency, accountability and regulatory compliance. Originality/value This paper offers a novel constraint-based reinterpretation of Kantian ethics that integrates adaptive mechanisms without collapsing into consequentialism, advancing the development of robust, policy-compliant and ethically grounded artificial intelligence systems.