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The exponential development of artificial intelligence (AI) technologies is reshaping societal decision-making with unprecedented breadth and depth. From risk assessment in financial credit, intelligent triage in healthcare, to auxiliary analysis in judicial sentencing and real-time traffic scheduling, algorithms have deeply embedded themselves into the core processes of social governance and public services and unleashed substantial efficiency gains (Lepri et al., 2018). However, this technological surge, while driving progress, has triggered a series of acute ethical challenges. Historical biases embedded in data are amplified by algorithms, perpetuating structural injustices (Giovanola & Tiribelli, 2022). Complex “black-box” models undermine decision explainability and traceability, thereby eroding the foundation of accountability (Wachter et al., 2017). In the pursuit of optimal performance, individual privacy and autonomy face erosion risks, with humans being reduced to mere data points (Radanliev, 2025). Although public opinion, academia, and policymaking circles have reached broad consensus on principles like fairness, explainability, and accountability (Jobin et al., 2019), mainstream engineering practices remain overly focused on accuracy and efficiency metrics, often failing to adequately address fundamental questions regarding human dignity and ethical boundaries. Educational initiatives aimed at raising awareness of these issues among AI stakeholders remain crucial for bridging the principle–practice gap (Bogina et al., 2022). For instance, in healthcare triage, an AI system optimized solely for survival probability may systematically deprioritize patients from underserved communities due to historical data gaps, perpetuating structural inequities that demand not merely post-hoc audits but embedded ethical safeguards (Giovanola & Tiribelli, 2023).
Prior research has produced substantive attempts to formalize or computationally operationalize Kantian ethics. Bringsjord et al. (2006) proposed an early logicist methodology for engineering ethically correct robots through formal reasoning architectures. Powers (2006) offered a foundational discussion of the prospects for a Kantian machine. Both approaches, however, exhibit significant limitations. Technical fixes, rooted in consequentialist logic, prioritize optimizing output statistics but generally neglect the legitimacy of decision motives and intrinsic respect for persons (White, 2022). Static legal provisions and ethical principles, when confronted with highly dynamic and contextually complex real-world algorithm applications, often reveal lags and rigidities and fall into “governance gaps.” Thus, a dynamic framework deeply embedded into technical architectures to protect innovation while achieving ethical self-consistency has become an urgent theoretical and practical imperative (Taddeo & Floridi, 2018).
Against this backdrop, Immanuel Kant's moral philosophy offers highly instructive theoretical resources for reconstructing the ethical foundations of AI. Its two core formulations—the Formula of Universal Law (“Act only according to that maxim whereby you can at the same time will that it should become a universal law”; Kant, 1785/2002, Ak. 4:421) and the Formula of Humanity (“act in such a way that you treat humanity ... never merely as a means to an end, but always at the same time as an end”; Ak. 4:429)—provide a normative perspective for evaluating algorithmic decisions that transcends outcome-based considerations. Can algorithmic rules withstand universalization tests? Do their operations respect each individual's value as an end? I do not claim that AI systems possess the autonomy, free will, or pure practical reason required for full Kantian moral agency (Chakraborty & Bhuyan, 2024; Manna & Nath, 2021). Rather, I follow White (2022) in arguing that Kantian principles can serve as necessary external design constraints and evaluation criteria for AI systems, constituting a feasible technical-ethical pathway, even in the absence of machine moral personhood.