As China rapidly advances in AI innovation and development, especially in frontier AI, its regulatory and ethical frameworks are under increasing pressure to ensure that technological progress aligns with human interests and societal values. This Article argues that AI value alignment—the process of ensuring AI systems act in accordance with human values, norms, and ethical principles—should be adopted as a strategic pillar in China’s evolving AI governance architecture. While China has already established a comprehensive legal, ethical, and self-regulatory landscape to address AI risks, these mechanisms often rely on reactive enforcement and external compliance. In contrast, AI value alignment offers a proactive, intrinsic approach that embeds safety and ethical constraints directly into AI systems, making them safer, more trustworthy, and responsive to human needs.

This study begins by mapping China’s current AI governance landscape, including national legislation such as the Cybersecurity Law, Personal Information Protection Law, and a growing set of regulations targeted at algorithms and generative AI. It also evaluates China’s normative commitments, such as the “human-centric” and “tech for good” principles articulated in national policy documents, and the increasing role of corporate self-regulation among major technology firms. While commendable in scope and ambition, these governance mechanisms often fall short in ensuring that AI behavior aligns with safety constraints and ethical intent—particularly when AI systems (such as agentic AI) become more autonomous and capable. This gap highlights the urgent need for a systematic value alignment strategy.

The Article then delves into the conceptual and technical foundations of AI value alignment, identifying both engineering challenges—such as reward misspecification, data bias, and model deception—and normative dilemmas, including moral pluralism, value aggregation, and dynamic ethics. Special attention is paid to frontier models like large language models and artificial general intelligence (AGI), which pose alignment challenges at a scale previously unseen. Drawing on contemporary alignment techniques such as RLHF (Reinforcement Learning from Human Feedback) and principle-based alignment, such as Anthropic’s Constitutional AI, the Article explores their limitations and calls for a more diversified, interdisciplinary, and forward-looking alignment research agenda.

Finally, the Article offers a roadmap for operationalizing AI value alignment across three key governance domains: Law and regulation, ethical norms, and industry self-regulation. Recommendations include the incorporation of alignment assessments into regulatory filings, the development of technical standards for value alignment and ethics-by-design guidelines, and institutional investments in safety and alignment research. The Article concludes by asserting that value alignment is not merely a technical safeguard but a governance imperative for the age of autonomous AI and agentic AI. By integrating alignment into its AI governance strategy, China can not only enhance domestic safety and public trust but also better coordinate with global AI ethics and safety initiatives—ultimately contributing to the shared goal of human-aligned and beneficial artificial intelligence.

The rapid development of generative artificial intelligence (AI) systems, particularly those fuelled by increasingly advanced large language models, has raised concerns of their potential risks among policymakers globally. In July 2023, Chinese regulators enacted the Interim Measures for the Management of Generative AI Services (“the Measures”). The Measures aim to mitigate various risks associated with public-facing generative AI services, particularly those concerning information content safety and security. China’s approach to regulating AI to date has sought to address the risks associated with rapidly advancing AI technologies while fostering innovation and development. Tensions between these policy objectives are reflected in the provisions of the Measures. As Beijing moves towards establishing a comprehensive legal framework for AI governance, there will be growing interest in how China’s approach may influence AI governance and regulation at a global level.

We present a practical verification method for safety analysis of the autonomous driving system (ADS). The main idea is to build a surrogate model that quantitatively depicts the behavior of an ADS in the specified traffic scenario. The safety properties proved in the resulting surrogate model apply to the original ADS with a probabilistic guarantee. Given the complexity of a traffic scenario in autonomous driving, our approach further partitions the parameter space of a traffic scenario for the ADS into safe sub-spaces with varying levels of guarantees and unsafe sub-spaces with confirmed counter-examples. Innovatively, the partitioning is based on a branching algorithm that features explainable AI methods. We demonstrate the utility of the proposed approach by evaluating safety properties on the state-of-the-art ADS Interfuser, with a variety of simulated traffic scenarios, and we show that our approach and existing ADS testing work complement each other. We certify five safe scenarios from the verification results and find out three sneaky behavior discrepancies in Interfuser which can hardly be detected by safety testing approaches.

In an era where artificial intelligence (AI) permeates every facet of our lives, the imperative to steer AI development toward enhancing human wellbeing has never been more critical. However, the development of such positive AI poses substantial challenges due to the current lack of mature methods for addressing the complexities that designing AI for wellbeing poses. This article presents and evaluates the positive AI design method aimed at addressing this gap. The method provides a human-centered process for translating wellbeing aspirations into concrete interventions. First, we explain the method’s key steps: (1) contextualizing, (2) operationalizing, (3) designing, and (4) implementing supported by (5) continuous measurement for iterative feedback cycles. We then present a multi-case study where novice designers applied the method, revealing strengths and weaknesses related to efficacy and usability. Next, an expert evaluation study assessed the quality of the case studies’ outcomes, rating them moderately high for feasibility, desirability, and plausibility of achieving intended wellbeing benefits. Together, these studies provide preliminary validation of the method’s ability to improve AI design, while identifying opportunities for enhancement. Building on these insights, we propose adaptations for future iterations of the method, such as the inclusion of wellbeing-related heuristics, suggesting promising avenues for future work. This human-centered approach shows promise for realizing a vision of “AI for wellbeing” that does not just avoid harm, but actively promotes human flourishing.