[author]Li Xueyao
[content]
Li Xueyao | Dynamic Evolution Framework and Institutional Design of Artificial Intelligence Legislation
*Author Li Xueyao
Professor at Koguan School of Law, Shanghai Jiao Tong University
Member of the Planning Committee of the Chinese Academy of Law and Social Sciences
Abstract: How to construct a legislative framework for artificial intelligence that combines stability and flexibility is a global issue. In response to the potential institutional application issues that may arise from the legislative approach of artificial intelligence under the guidance of normative methodology, an "adaptive law" legislative approach should be adopted. In order to achieve dynamic adaptation of legal rules and technological evolution, it is also necessary to explore the "adaptive rule of law" path of artificial intelligence legislation in combination with local practices: cautiously treat the legislative goals of systematization and departmental legalization, and try to achieve the legislative goals of artificial intelligence in the substantive legal framework of traditional departmental laws by adopting the method of legislative reform or abolition; The principle of dynamic adaptability should be the core principle of artificial intelligence legislation; The drafting of clauses should shift from "obligation based" to "behavioral incentives"; The focus of theoretical exposition should be on how to establish a multi-level governance system of "rule of law", "central bottom line rules+local differentiated pilot+judicial precedent guidance", and further optimize the Chinese style rule of law practice, including "coordination of soft and hard laws". This can not only continue the traditional institutional innovation of "experiment promotion" since China's reform and opening up, but also attempt to contribute universally valuable institutional analysis tools to global technological governance.
1. Problem posing
The exponential development of artificial intelligence technology is reshaping the global socio-economic structure, and its characteristics of autonomous decision-making, cross domain penetration, and rapid iteration are posing an unprecedented adaptability crisis to the traditional legal system. The legislation of artificial intelligence has recently become a hot topic in the Chinese legal community, among which many researchers tend to construct a "Artificial Intelligence Law" with basic legal nature, and achieve systematic regulation of artificial intelligence governance through systematic legislation.
Artificial intelligence legislation needs to break through the path dependence of the "codification like" paradigm and shift towards a more flexible "adaptive rule of law" model that is "anti fragile", adopting a dynamic legislative approach that enhances the system's ability to resist risks through local trial and error. This theoretical construction is based on the following practical contradictions: on the one hand, the high-speed iteration of technology requires the law to maintain dynamic adaptability, and requires governance frameworks to have mechanisms compatible with incentives; On the other hand, the social embeddedness of algorithms (such as medical diagnosis, judicial adjudication, financial risk control) urgently requires laws to establish an unbreakable bottom line for public interest and social ethics. More importantly, the research and practice of artificial intelligence law in the sense of standardized methods still need and can basically rely on traditional departmental legal systems such as civil law and administrative law. If we rashly promote the systematic legislation of artificial intelligence law with an "independent departmental legalization" approach without a systematic theoretical construction, the result is likely to be a "layered" legislation, which will not only undermine the integrity of the existing legal system, weaken the continuity and certainty of the existing legal system, but also bring high cost compliance obligations of different regulatory departments to various entities in the artificial intelligence industry, and ultimately lead to the opposite of creating a good innovation and promoting institutional environment.
To resolve this contradiction, this article advocates: firstly, in terms of legislative philosophy, further determination should be made that artificial intelligence legislation does not aim for "top-down" systematic legislation, but focuses on problem awareness and follows a dynamic legislative model of "adaptive legislation (law revision) - experimental practice - feedback based law revision". Adhering to the principles of "no legislation unless necessary" and "no legislation if policy measures can be used to replace it", legislative goals should be achieved as much as possible within the substantive legal framework of traditional departmental laws through legislative reform, abolition, and interpretation. Secondly, regarding the practical legislative work of artificial intelligence, it should follow the legislative rules under the concept of "adaptive rule of law", based on pre procedures such as "scientific evaluation of the applicability of the legal system", and mainly revise existing laws and regulations through adaptability, supplemented by drafting necessary separate laws, regulations or administrative regulations, such as the "Artificial Intelligence Ethics Law" or other special laws. Thirdly, when modifying specific provisions of laws and regulations or designing civil model laws, we should always adhere to the governance framework of "simple rules behavior incentives diverse collaboration", and strive to preserve the openness of legal rules by setting minimum security and ethical bottom lines (such as algorithm transparency and data sovereignty ownership); Utilize various behavioral incentive measures (such as safety credit ratings) and other tools to construct a mechanism for internalizing external risks; Relying on local government pilot programs, judicial precedent feedback, and industry standard setting to jointly promote rule improvement, and so on.
This article attempts to conduct innovative theoretical operations in the following three aspects: firstly, in terms of methodology, it integrates the theory of complex adaptive systems and behavioral economics, viewing law as a dynamic network under the game of multiple subjects, breaking through the static analysis of traditional legal doctrine, and surpassing the institutional design thinking dominated by market incentives in traditional legal and innovation research; Secondly, on the practical path, we will refine China's "experiment promotion" governance tradition and construct a collaborative mechanism of "central bottom line constraints local scenario pilot enterprise compliance innovation", providing a non Western centric paradigm for global technological governance; Thirdly, in terms of institutional design, it is proposed to use "behavioral incentives" as the main tool, focusing on the mechanism design direction of internalizing external risks of artificial intelligence, and achieving an organic connection between industry and enterprise self-discipline and national legislation.
2. Adaptive method based on the demand for adaptive governance of artificial intelligence
The emergence and irreversibility of artificial intelligence technology are disrupting the cognitive paradigm of traditional law. As deep learning models continue to evolve beyond the predetermined logic of human designers, and algorithm decisions are deeply embedded in the "capillaries" of public governance and private life, the lag of law is no longer limited to the "gap between rules and facts", but evolves into a structural conflict between "institutional rationality and technological wildness". In this context, traditional legal theories in regulating the artificial intelligence industry are naturally prone to triple adaptability dilemmas: the temporal and spatial disconnect between static rules and dynamic technology, the power mismatch between centralized legislation and distributed innovation, and the value tearing between formal justice and algorithmic "tyranny". In response to this crisis, this article proposes the theories of "adaptive law" and "adaptive rule of law" - a paradigm reform based on complex adaptive systems, driven by multi-level feedback, and aimed at the common evolution of "technology law society". On this basis, the rule of law in the era of artificial intelligence is "restructured".
2.1 The demand for legal "adaptability" in artificial intelligence governance
Artificial intelligence is changing the form of social production and communication in highly dynamic and unpredictable ways. In this context, the law is no longer able to effectively respond to social changes through one-time, static, and rigid rule design. If the "centralized fixed" legislative approach is still used to comprehensively cover a wide range of application scenarios, there is inevitably a risk of legal lag or excessive constraints on innovative activities. Based on this, there is an increasingly prominent demand for an "adaptive" paradigm in the legal field, which means that as an important node in the complex application system of "technology law society", the legal system needs to respond to rapidly changing and diverse technological needs through a paradigm of "multiple iterations and gradual improvement"; It is necessary to embed reflexive tools such as "legislative sandbox" and "algorithm impact assessment" to enable the law to have the ability of "self observation, self-criticism, and self-renewal". Specifically, the demand for this adaptive paradigm shift stems from the unprecedented uncertainty and unexplainability of artificial intelligence at the technical level. Artificial intelligence systems constantly engage in self-learning and parameter optimization during operation, and issues such as algorithm "black box" and insufficient "interpretability" pose serious challenges to the pre-set legal accountability and compliance boundaries. This determines that if the law follows the traditional "exhaustive" legislative model, it will inevitably lead to the risk of content lag. Furthermore, artificial intelligence applications span across many fields such as healthcare, transportation, finance, and public services, with varying regulatory requirements and risk sensitivities in different scenarios. Flexible self adjustment mechanisms must be relied upon to match specific needs. In addition, enterprises have accumulated a large amount of application experience in market competition and independent compliance innovation. If the law lacks an effective feedback loop ("production mechanism") and cannot timely absorb these experiences, it may lead to institutional design deviating from actual operation. Therefore, it is of great practical necessity to construct a legal model that allows for "trial and error, gradual revision" and continuous updating and improvement through multiple feedback from the market and society.
2.2 Theoretical connotation of adaptive method
Adaptive law is an upgraded version of responsive law. Looking back at the early arguments of scholars such as Nonette and Selznick, it can be found that responsive law, as a theoretical achievement of legal realism, emphasizes making the legal system open and flexible, and "responding more to social needs". This need includes breaking away from the closed form of being purely a tool of state control, and instead actively engaging with social interests and concerns, in order to better adapt to social needs. The open demands of responsive law seem to imply that this theory is applicable to regulatory scenarios involving big data or artificial intelligence. However, the reform approach of traditional responsive law relies more on the "passive absorption" of social opinions by the public sector, which means that legislative or judicial institutions revise existing rules after social issues become prominent. In contrast, adaptive law not only emphasizes the sensitivity of the law to social needs, but also delves deeper into theories such as complex adaptive systems, viewing the endogenous (based on the needs of the parties) judicial innovation judgments, corporate compliance measures, and local legislative experiments in social and technological systems as important "self-organizing" forces in legal evolution. Under this approach, adaptive law not only requires legislative and enforcement departments to be open and inclusive in receiving social feedback, but also encourages platform enterprises, industry associations, and public organizations to jointly promote the formation of norms. For example, companies can take the lead in implementing certain data protection, ethical review, open source community, and algorithm review systems internally through compliance innovation and establishing self regulatory rules. If these practices are proven effective, judicial or legislative bodies can refine them into more universal legal standards. Local governments can also conduct local verification of new rules through regional legislation, regulatory sandboxes, and other forms, and gradually promote them to a larger scale. In this way, the law is no longer a passive repair that waits for "social demands to be reflected", but rather creates conditions in advance, allowing multiple parties to form pragmatic and flexible rule designs in a "trial and error" mechanism. Compared with the responsive approach, another important feature of the adaptive approach is that it places more emphasis on balancing diverse interests and high-frequency iterations. The responsive approach emphasizes more on the immediate response of the law to specific social issues, while the adaptive approach attempts to institutionalize and proceduralize this "response" so that it can complete a continuous cycle of "problem identification norm revision re practice re revision" in a short period of time when facing rapidly changing scenarios. Through this cycle, the law can repeatedly weigh multiple goals such as algorithm fairness, data sovereignty, ethical values, and industry incentives at different stages, and continuously adjust the proportion based on actual effects, thus avoiding the embarrassing situation of "the law just being promulgated falling behind technological development".
2.3 The response and shortcomings of existing legal theories to the demand for adaptive law
Before exploring why adaptive law is needed and how to meet its demands, the mainstream legal community has formed various ideas and theories, attempting to respond from different dimensions to how law can face the adaptive challenges brought by high dynamic and complex environments. Although these theories are not entirely targeted at the artificial intelligence scenario, they provide a foundation for the construction of adaptive law in legal methodology. Firstly, the theory of legal argumentation and the principle of proportionality, as a practical tool, are often used by scholars as academic tools to address legal issues. The theory of legal argumentation emphasizes the organic coordination of different values and interests in the judicial or legislative process through rigorous reasoning procedures. One of the most representative ones is the "proportionality principle" widely used in the fields of constitutional law and administrative law. Through the three-level structure of "appropriateness necessity measurability", legislators or judges can try to measure the value in the design of articles or judicial rulings when facing risks and demands for technological innovation in artificial intelligence technology, thus avoiding simplified one size fits all or excessive regulation. Although such claims provide an operational analytical framework for balancing conflicts of interest in the application of artificial intelligence in law, they emphasize more on the matching of post judgment or abstract principles with specific rules, making it difficult to apply to informal institutions and unable to fully meet the practical needs of "continuous iteration and rapid feedback" in the context of artificial intelligence. Secondly, systems law provides the most systematic theoretical explanation for the self maintenance and functional adaptation of the legal system in highly complex societies. Luhmann et al.'s research regards law as a subsystem in the social system network, emphasizing that law must selectively respond to changes in the external environment through its own procedures, symbols, and norms to maintain the self reproduction of the system. In the context of the rapid development of artificial intelligence, systems law reminds us that the law should not only make "structural coupling" adjustments to cope with external technological shocks, but also retain a considerable degree of self referential nature to avoid being carried away by technological demands and losing the autonomous logic of the law. In addition, the reduction of complexity reflected in systems law is also an important technical issue in artificial intelligence regulation or legislation. However, when systems law is applied in the field of technology, it often faces criticism for being too abstract and lacking guidance on specific institutional design, making it difficult to provide detailed plans for the implementation of the "gradual pilot feedback correction" mechanism. Finally, soft law theory has received significant attention in both international and domestic legal research in recent years. Soft law usually refers to a series of rules or guidelines that do not have traditional state coercive power but can have important binding or guiding effects in practice, such as industry self-discipline conventions, technical standards, government guidance opinions, etc. Artificial intelligence legislation needs to maintain moderate flexibility in a rapidly evolving technological environment. Soft law is highly favored due to its low development cost, rapid updates, and flexible constraints on multiple subjects. However, the uncertainty of the legal status and effectiveness of soft law may result in a lack of rigid protection in dealing with issues such as algorithmic discrimination and data abuse, leading to issues of avoidance of application or insufficient enforcement. Therefore, in order to effectively utilize soft law, more refined supporting mechanism design is needed - such as coordinating soft law with hard law, enabling industry standards or guidelines to be transformed into mandatory rules through judicial or administrative procedures when necessary, establishing dynamic evaluation mechanisms, and updating systems in a timely manner. In addition to the main threads mentioned above, there are also many other theoretical schools in the legal community, such as legal pluralism advocating the recognition of the legitimate status of local, collective, or industry-specific rules outside the formal legal system, thus forming a "multi center multi voice" legal ecology; Legal pragmatism emphasizes the flexible adoption of any norms or technological means that can solve real-world problems in specific fields. In addition, some scholars have explored the preventive rule of law and the prior regulation of the law. These studies interpret from different perspectives how law can maintain a positive connection or interaction with rapidly changing social realities, providing diverse knowledge resources for the development of adaptive law.
2.4 Theoretical response and shortcomings of existing research on the relationship between law and technological innovation
In the field of social sciences, there are three main theoretical frameworks that directly focus on the relationship between law and technological innovation: innovation economics, science and technology law (or technology law), and intellectual property law. The above theoretical framework provides multiple perspectives for understanding and regulating the dynamic relationship between technological innovation and legal systems. However, facing the rapid iteration of artificial intelligence technology, complex ethical challenges, and the need for multi-party collaborative governance, these theories still have certain limitations and urgently need to be further expanded in depth and breadth.
Firstly, the contribution and shortcomings of innovation economics. Innovation economics integrates relevant research results from new institutional economics, national innovation systems, and law and finance. It comprehensively applies theoretical deduction and empirical research methods, and provides an important analytical framework for understanding how institutions affect technological innovation through theoretical tools such as property rights definition, transaction cost analysis, and contract execution. New institutional economics emphasizes that by clarifying property rights relationships and improving market rules, transaction costs in the innovation process can be reduced, providing institutional incentives for enterprise research and technological progress. For example, the protection of property rights and the improvement of intellectual property systems are seen as important foundations for incentivizing corporate R&D investment. Although this theory has also been influenced by behavioral economics and evolutionary economics in recent years, its core is still based on the assumption of rational people and market incentive logic, mainly focusing on economic efficiency and market mechanisms. When dealing with highly uncertain and complex social goals in artificial intelligence technology, especially when explaining economic development and technological progress issues in developing countries such as China, this framework often falls short. In the field of artificial intelligence, there are common issues such as algorithmic black boxes and ethical externalities, and relying solely on property incentives is not enough to solve the problem of social costs. In addition, although new institutional economics focuses on informal institutions such as morality and religion, its understanding of the role of law is mostly limited to economic regulation, and has not fully responded to the dynamic game between ethical values, public trust, and multiple stakeholders in artificial intelligence governance. For example, although North acknowledges that human decision-making rationality is influenced by institutional constraints and incomplete information, he still assumes that market entities will make optimal decisions based on "cost-benefit", while the government is primarily responsible for establishing property rights and correcting market failures. This assumption is significantly limited in its applicability in complex and immature artificial intelligence governance contexts. In addition, the National Innovation System Theory, as an important theoretical component of innovation economics, is also a complex system application theory that focuses on enterprise innovation and learning processes, emphasizing the construction of an ecosystem conducive to innovation through technological infrastructure, education systems, and policy support. This approach shares similarities with the adaptive rule of law theory presented in this article, but this theory pays too little direct attention to the legal system, and there is still a lot of research work needed to translate its research results into legal achievements.
Secondly, the cutting-edge exploration and response capabilities of science and technology law (or technology law) are insufficient. Science and technology law focuses on the two-way interaction between law and technology, emphasizing the regulation of technological risks and the promotion of technological innovation through interdisciplinary perspectives. However, science and technology law often focuses on the characteristics and development paths of technology, but has not fully absorbed the analytical methods of sociology and economics, resulting in a lack of operational solutions in the institutional design that balances technological innovation and social value. Especially in the governance of artificial intelligence, the research on science and technology law theory focuses more on technology specific legal issues (such as algorithm transparency or data governance), and less on analyzing how law dynamically adapts among stakeholders from a complex system perspective. In addition, technology law often relies on linear logic in the design of specific rules, that is, legal rules directly intervene in technological development, and fail to fully consider the "non-linear iteration" and complex social needs of artificial intelligence technology. Although flexible governance tools such as soft law and regulatory sandboxes have been widely mentioned in research in this field, the compatibility issues between the specific institutionalization paths of these tools and the legal system have not been fully addressed.
Finally, the contribution and adaptability challenges of intellectual property law. Intellectual property law has long been a core field for studying the relationship between technological innovation and law. It provides important legal support for technological progress by clarifying the ownership of innovative achievements and establishing a balance mechanism between technology diffusion and innovation protection. In the field of artificial intelligence, the focus of intellectual property law is on the protectiveness of algorithms, the attribution of works in generative artificial intelligence, and issues of data sharing and monopoly. However, in the face of the "dynamic generation" and "multi-agent collaboration" characteristics of artificial intelligence, the adaptability of the intellectual property system faces a dual challenge: firstly, how to protect the legitimate rights and interests of innovators while preventing data isolation and technological monopoly caused by excessive protection, and traditional intellectual property laws are also difficult to find accurate adjustment solutions; Secondly, intellectual property law is mostly guided by market logic, with relatively little consideration for the ethics of artificial intelligence, and there is a lack of public participation mechanisms for institutional improvement. In addition, the content produced by generative artificial intelligence may have exceeded the protection logic of traditional intellectual property rights. How to strike a balance between protecting innovation incentives and maintaining public interests has become an urgent institutional challenge.
3. Adaptive Rule of Law from the Perspective of Behavioral Incentives
To compensate for the above limitations, the theory of law and technological innovation can be expanded in the context of cutting-edge technology regulation, incorporating complex adaptive systems into the analytical framework, and combining "behavioral incentives" to replace or expand the traditional "market incentive" logic, forming a behavioral law and economics version of the "adaptive governance" model. Based on this, the ideal types of "adaptive law" and "adaptive rule of law" can be further constructed. This model not only inherits the concerns and propositions of new institutional economics and innovative economics on legal adaptability, but also better adapts to the self-learning nature of artificial intelligence technology and the needs of multi stakeholder collaborative governance. Of course, some scholars have recently conducted the following research: on the one hand, they have attempted to go beyond legal and technological innovation theories and instead adopt mechanism design theories that are well utilized by new institutional economics, focusing on information asymmetry and rule optimization issues in the field of artificial intelligence governance, in order to solve the problem of incentive failure in social regulatory fields such as artificial intelligence ethics; On the other hand, attempting to transcend the binary opposition between security and development, and re understanding the technical principles and risk complexity of artificial intelligence from a communication perspective, in order to form rules in dynamic processes. On the basis of these theories, adaptive rule of law pays more attention to "behavioral incentives", that is, it pays more attention to the psychological and cognitive biases of individual behavior, and achieves social goals through behavioral adjustment.
3.1 The Ideal Type Construction of Adaptive Rule of Law
Firstly, introduce the theory of complex adaptive systems: multi-agent feedback and self-organization. Complex adaptive systems emphasize the existence of multiple agents within the system, which can achieve global or structural evolution through interaction and feedback under relatively dispersed conditions. From a regulatory perspective, the law can integrate local enterprise innovation pilot projects, user feedback, industry standard revisions, etc. into a sustainable evolutionary system by setting simple and core "basic rules" and "consensus goals". Legislation can adopt the ideas of "cooperative governance" and "branch governance", no longer a top-down "one size fits all" approach, but gradually pilot in different business branches, regions, and technical fields, promoting the integration of local successful experiences into the overall legal framework.
Secondly, grafting behavioral economics: from "market incentives" to "behavioral incentives". Behavioral economics reminds us that market entities often make decisions based on factors such as psychological preferences, social norms, and cognitive biases, rather than pure rationality. The same applies to artificial intelligence governance. Algorithm developers, platform companies, or the public are not only concerned with economic benefits, but also influenced by multiple factors such as compliance reputation, ethical review, and user perception. Therefore, the law should appropriately establish incentive mechanisms and "nudging" or "soft incentive" strategies to enable enterprises and users to apply artificial intelligence more safely and responsibly in a relatively autonomous environment. For example, through tax incentives, algorithmic security ratings, ethical certification labels, and other means, positive incentives can be provided to law-abiding entities; For technology users who violate regulations or are irresponsible, multiple tools such as administrative, judicial, and credit penalties can be used to achieve behavioral constraints.
Thirdly, the combination of "sophisticated regulation" and "adaptive regulation". Exquisite regulation "is an optimized regulatory approach based on data and technology in the academic community in recent years. It is a concept proposed after exploring minimal government intervention, greater flexibility, precision, and multi-party hierarchical collaborative regulation. Exquisite supervision "emphasizes the use of technologies such as big data and artificial intelligence to assist regulatory departments in improving the efficiency of problem detection and resolution, while also focusing on providing more open institutional space for corporate compliance innovation. Incorporating "sophisticated regulation" into the framework of "adaptive regulation" can further broaden the application boundaries of tools such as "regulatory sandbox" and "regulatory technology", complete "rule testing risk assessment institutional iteration" in a short period of time, and maintain a closer connection between the pace of legal and technological development.
Fourthly, establish a coordination mechanism between the principles of adaptability and prevention. The principles of adaptability and prevention have their own emphasis in technological governance, and they are not mutually exclusive. Instead, different regulatory principles can be selected based on the nature of risks and social values. Generally speaking, the precautionary principle emphasizes that when there is insufficient scientific evidence but the potential harm is enormous, legislators or regulators should adopt more cautious or strict constraints to avoid irreversible public safety or ecological damage. This often requires imposing prior restrictions or even temporarily suspending the application of certain high-risk technologies. In contrast, the principle of adaptability focuses more on synchronizing regulatory rules and technological progress through phased pilot and dynamic iteration in situations of high uncertainty, thereby preserving technological innovation space while ensuring safety. For innovative technologies that are difficult to form scientific consensus in the short term but can be effectively evaluated and controlled for risks through "small-scale experiments and gradual promotion", adopting the principle of adaptability for regulation is the most appropriate. And once the technology itself may pose a "deep and irreversible" threat, or involve ethical bottom lines of high public concern, the precautionary principle will take the lead, locking in the risk threshold with rigid legislation first. By comprehensively applying the above two principles, legislators can achieve a more balanced governance effect between security and continuous innovation.
Fifth, the embedding of social values and the balance of multidimensional goals. As mentioned earlier, institutional economics often constructs rules around two core objectives: efficiency and property protection. In the era of artificial intelligence, laws need to incorporate more social goals such as public trust, ethical protection, national security, and fair competition. After introducing the perspectives of complex adaptive systems and behavioral economics, the law not only needs to regulate the economic behavior of market entities, but also needs to guide them to consciously assume social responsibilities in data processing, algorithm training, and application scenario expansion through behavioral incentive strategies. Therefore, it is necessary to expand the research object of artificial intelligence law to include institutional design such as ethical review and value alignment of artificial intelligence.
3.2 The methodology of "Simple Rules - Multiple Iterations"
Firstly, from static constraints to dynamic evolution. Traditional legal rules often rely on fixed and detailed clause designs, aiming to regulate new technological risks in a comprehensive manner. However, the "exponential" evolution speed of artificial intelligence technology makes it difficult for legislators to capture emerging issues in real time, leading to delays or loopholes in laws quickly after they are introduced. On the other hand, adaptive regulation encourages "setting simple and sufficient bottom line rules first, and then optimizing them through multiple iterations", allowing enterprises, research institutions, and local governments to conduct exploratory technical practices under basic constraints, and make regulatory adjustments after accumulating sufficient experience. Through continuous revision, the law can not only provide compliance guidance for the market in the early stages, but also retain sufficient tolerance for technological innovation in subsequent evolution.
Secondly, the connotations of "simple rules" and "behavioral incentives". Simple rules "can be seen as a" reward function "similar to reinforcement learning in the legal system: legislators do not design cumbersome provisions that cover all contexts in advance, but set several key goals or bottom lines, and use the simplest way to indicate what" compliance "or" violation of public interest "means. Just as in reinforcement learning, intelligent agents continuously optimize their strategies based on reward or punishment signals, laws can also use this type of minimization constraint to drive social agents to find better compliance paths in complex and dynamic scenarios. In this way, legislation only needs to clarify the core values that should be upheld (such as algorithmic fairness, data security, ethical red lines), without making exhaustive provisions for each specific step. This mechanism can provide ample exploration space for technology and the market, allowing "adaptation" to naturally emerge through continuous experimentation and feedback. Since simple rules are not sufficient to maintain the benign evolution of complex systems, it is necessary to carry out a "reward punishment" design, that is, to establish a "behavioral incentive" mechanism. In other words, the law encourages enterprises, research institutions, and even the public to actively explore innovative solutions with higher standards and greater social responsibility by providing diverse positive incentives (such as tax incentives, public procurement priorities, ethical certification labels) and negative sanctions (such as credit penalties, administrative penalties) while adhering to the minimum institutional requirements. Similar to the dynamic tuning process in reinforcement learning, these incentive tools will continuously calibrate their effectiveness in practice: if there is technical abuse or improper behavior, punishment will be triggered immediately and developers will be reminded to make corrections; If compliance innovation brings significant safety benefits, positive incentives will further enhance the sustainability of the model. Through the dual path of "simplest rules+iterative incentives", law is no longer a mechanical controller in complex adaptive systems, but a "coach" for setting direction and goals, helping the entire socio technical system gradually step onto an ideal track that combines safety and innovation through repeated trial and error.
Thirdly, "multiple iterations" and iterative adaptation. Drawing on the concept of reinforcement learning, the legal system can regard "simple rules" as the cornerstone of a reward function and achieve dynamic optimization through a cycle of "exploration feedback revision". In the "exploration" stage, enterprises, local governments and other entities carry out activities such as product landing and regulatory sandbox pilot under established minimum rules and core bottom lines, just like intelligent agents testing different strategies in the environment to obtain preliminary feedback; Subsequently, in the "feedback" stage, feedback signals from multiple channels such as judicial precedents, social public opinion, and user experience, similar to reward or punishment signals, provide evaluation criteria for the effectiveness of current rules and reveal potential gaps or innovative highlights in the rules; Finally, in the "revision" stage, legislators adjust the system based on these feedbacks: if the feedback shows that the safety bottom line is set too loosely or ethical risks have not been fully reflected, the regulations should be tightened in a timely manner; On the contrary, if no serious conflicts are observed in actual operation, the flexibility space of the system will continue to be retained or compliance entities will be further encouraged to pursue higher standards.
In summary, "adaptive rule of law" is a proposition based on interdisciplinary transformation of theories related to the relationship between law and innovation, such as innovative economics and new institutional economics. It no longer focuses solely on market efficiency or investment protection, but advocates for the organic integration of algorithm iteration, technological ethics, social trust, and multi-party collaborative governance into regulatory logic. The core of adaptive rule of law lies in "simple rules - multiple iterations", and through mechanisms similar to behavior incentive design in reinforcement learning (combining positive incentives with negative sanctions), it guides all participating entities to achieve a balance between compliance and innovation in the rapidly changing technological ecosystem. Although new institutional economics has certain advantages in focusing on legal adaptability, empirical research, and institutional incentive effects, only by integrating dynamic adjustment elements advocated by complex adaptive systems and behavioral economics on this basis can we more effectively address the challenges brought by multi link and multi subject collaborative governance in the era of artificial intelligence. This process aligns the concept of "adaptive rule of law" not only with the academic advocacy for intelligent regulation and branching governance, but also has the potential to become a highly innovative institutional path in the field of artificial intelligence legal governance in China and even globally.
4. Preliminary Application of Adaptive Rule of Law Theory in the Field of Artificial Intelligence Legislation
In order to more "down-to-earth" integrate basic theories into legislative work, the following article will make a preliminary discussion on the application of adaptive rule of law theory in the field of artificial intelligence legislation from the perspective of "institutional generation mechanism". As mentioned earlier, this article opposes "top-down" systematic legislation, that is, opposes the formulation of a specialized "artificial intelligence law". Therefore, the following discussion on the drafting of specific provisions is mainly conducted from the perspective of drafting a civil "model law", with the aim of providing a systematic reference for relevant departments to conduct "applicability testing and updating (interpretation, reform, legislation, abolition)" of existing substantive laws.
4.1 Adaptive generation mechanism of artificial intelligence legal system
In order to ensure that the legal system of artificial intelligence can continuously adjust with the development of technology, this article advocates summarizing the existing domestic and foreign experience in the construction of international science and technology innovation centers, constructing a multi-level legal adaptability mechanism, and enabling the generation and adjustment of legal rules to cover different levels of legal operation modes.
Firstly, further clarification of the legislative approach that combines bottom line and openness. At the level of the National People's Congress, by issuing normative legal documents adapted to AI technology, the legislative idea of establishing a bottom line and opening up is clearly established. As emphasized in the previous text, artificial intelligence legislation cannot cope with the new risks brought about by the rapid evolution of technology if it relies solely on static and detailed code rules. The legislative approach needs to be adjusted. For example, according to the principle of dynamic adaptability, the State Council can be required to continuously update the "minimum but necessary" bottom line rules through rolling legislation, such as setting algorithm security obligations, privacy protection, and ethical red lines, to ensure that public interests and social values are not significantly damaged, through a special authorization form with additional time limits; At the same time, sufficient flexibility and revision space should be reserved in the specific institutional design, so that legislation can be updated and supplemented in a timely manner after industrial practice and judicial precedent feedback. In two expert suggested drafts, scholars have attempted to systematically design key aspects such as data protection, algorithm evaluation, ethical review, and artificial intelligence product management, which to some extent fills the gaps in existing laws. However, from the perspective of adaptive regulation, these proposed drafts need to further reflect the legislative logic of "basic bottom line+phased adjustment" in terms of the "package" of compliance obligations and provisions.
Secondly, further leverage the role of judicial judgments in the generation of systems. It is generally believed that, at the end of the 20th century and the beginning of the 21st century, the court systems of the United States and China, under the idea of coordination with the legislative and administrative organs, used the "adaptive mechanism" of the judicial process, that is, to supply the system from the bottom up according to the needs of the parties, actively reduce the responsibilities of the enterprises in Taiwan and their compliance costs, reduce the harsh privacy protection standards, and adopt more reasonable intellectual property protection benchmarks, providing timely system supply for financial innovations such as venture capital necessary for scientific and technological innovation. In particular, through the judicial decisions to create the applicable safe harbor rules and red flag standards, the Internet platforms of the two countries have developed rapidly with the help of this exemption clause. In view of this, how to continue the judicial tradition of "adaptive institutional supply" in the fields of network platform economy and financial innovation in China's courts is a theoretical issue that the legal community must attach importance to. In the short to medium term, we can actively promote institutional improvement in several specific aspects, including but not limited to: (1) China's legislative body should adopt an open attitude and go beyond existing legal reservation theories, especially the institutional positioning of "civil law countries" and "written law countries". The application of judicial interpretations and guiding cases in the field of technological innovation adopts an expansive legal interpretation approach, maintaining a more tolerant attitude. (2) In the field of technological innovation, Chinese courts should continue to consciously and actively play the role of "policy courts", summarize their experience in promoting comprehensive innovation, and actively provide responsive systems. The legislative body should also timely summarize or solidify the institutional experience of collaboration between the court and legislative, administrative and other agencies. (3) In terms of performance evaluation system, more space for independent innovation and exploration should be given to the presiding judges or collegiate bench on the front line of the trial. For example, for the judges of professional courts and professional courts involved in innovation fields such as intellectual property, finance and Internet trials, the personnel management system of "no quantitative assessment" can be explored. (4) From the perspective of improving the quality of judicial documents related to technological innovation, it is mandatory for judges engaged in technological innovation trials to have a degree in natural sciences to prevent deviations in knowledge structure from affecting the scientificity of judicial decisions.
Thirdly, ensure and optimize the self generating mechanism of the technology enterprise system. In the rapidly evolving artificial intelligence ecosystem, large platform enterprises often stay at the forefront of technology and compliance practices due to their mastery of key elements such as algorithms and data. In the absence of comprehensive legal norms, these platforms can shape industry autonomous rules through self regulatory measures such as internal ethical review, black box testing, open interfaces, or algorithm interpretation mechanisms. The law should give enterprises more space for institutional innovation and encourage them to form artificial intelligence governance standards that meet market demand through industry self-discipline. Specifically, it includes two steps: first, independent compliance and industry demonstration: In order to avoid reputation risks and potential litigation pressures, enterprises are usually willing to ensure algorithm security, transparency, and compliance through internal institutional innovation. If their effectiveness is good, these practices have demonstrative significance in the industry. Secondly, incorporating public rules: After comprehensively evaluating the leading practices of these enterprises, the government or legislative body can "legalize" their excellent experiences, such as issuing departmental regulations, standard guidance documents, or stating recognition and promotion of these self regulatory standards in national legislation. This can complete the transformation process from "enterprise standards" to "public regulations". However, this mechanism must be used cautiously to prevent companies from evading substantive legal responsibilities through formal compliance actions. It is possible to establish a "dynamic compliance review mechanism" to ensure that enterprises still bear corresponding legal obligations in the innovation process, and to curb their behavior of exploiting policy arbitrage and market monopoly.
Fourth, pilot and expand local experimental legislation. Based on the nonlinear iterative characteristics of artificial intelligence technology, a closed loop of "policy experimentation feedback learning rule iteration" is constructed to reduce systemic risks and accelerate the synchronous evolution of law and technology through small-scale trial and error. Faced with the economic foundation and industrial structure of different regions, local governments can rely on local legislative mechanisms to conduct differentiated pilot projects for some artificial intelligence application scenarios (such as autonomous driving, intelligent healthcare, cross-border data flow, etc.), in order to obtain social governance feedback. Specifically, it also includes two steps. One is differentiation pilot: by setting different evaluation indicators and compliance requirements for different scenarios, local governments can verify the feasibility of specific institutional designs and make timely adjustments. Secondly, replication and promotion of achievements: When the institutional practice of a pilot project in a certain region is repeatedly verified to be effective, it can be elevated to a higher level of legislation or promoted on a larger scale. If the pilot program fails or significant risks arise, society will only bear the risks and losses on a small scale, avoiding a nationwide institutional failure like a domino effect. In the future, it can be considered to establish "Artificial Intelligence Legal Pilot Zones" at the national level through amendments to the Legislation Law, allowing local governments to carry out greater institutional innovation in artificial intelligence governance, and timely transform local legislative experience into national legal rules through regular evaluation and feedback mechanisms.
Fifth, establish a multi stakeholder expert organization mechanism. Artificial intelligence legislation also requires the participation of multiple stakeholders such as industry organizations, research institutions, and the public to ensure scientific and transparent decision-making. It may be considered to set up an "AI Legislative Adaptability Evaluation Committee" under the Legal Working Committee of the National People's Congress, organize interdisciplinary expert evaluation and public hearings and other procedures according to the needs of the situation, and realize the tripartite dialogue among technology, law and society through a procedural way. The evaluation committee may regularly conduct quantitative or qualitative assessments of the suitability of all existing substantive laws and propose amendments or administrative guidelines to maintain the forward-looking and feasibility of the law at different stages of technological transition.
4.2 Implementation of the principle of dynamic adaptability
Firstly, highlight the modular control of high-risk scenarios. There have been various expert proposals on artificial intelligence legislation that have been publicly released. Although they mention high-risk scenarios such as autonomous driving and medical AI, most of them regulate risky behavior based on "principle requirements" or relatively general responsibility attribution, and rely more on linear classification and grading principles. If the system for "minimum safety and ethical bottom line" can be refined in the future, supplemented by operable control processes or compliance standards (such as testing conditions, external audits, and in-process monitoring mechanisms), it will help provide clearer behavioral boundaries for various industries. At the same time, legislation could consider explicitly authorizing local governments or industry regulatory authorities to conduct "pilot testing" of high-risk artificial intelligence and roll out revisions of safety standards, verifying the feasibility of compliance through pilot testing.
Secondly, preserve institutional flexibility and update mechanisms for cutting-edge technologies. For various reasons, current laws and regulations have not yet established clear review and evaluation paths for cutting-edge technologies such as multimodal generative artificial intelligence, emotion computing, and brain computer interfaces. In order to better promote innovation and effectively control risks, it is possible to consider setting "regular technical evaluation+dynamic article update" clauses in relevant single line laws, regulations, and administrative rules, allowing legal articles to be revised within a certain period based on recommendations from specialized committees or administrative authorities, social surveys, and industry empirical data; At the same time, in order to more effectively address the potential ethical and security risks brought by emerging technologies, relevant issues should be promptly included in the legislative scope.
Thirdly, strengthen diversified information disclosure and external supervision. Existing legislative research on artificial intelligence emphasizes the algorithm transparency and data compliance obligations of platforms and developers, but mostly starts from traditional regulatory thinking, embedding responsibilities into "top-down" mechanisms such as "approval, filing, and commitment". If relevant legislation can further clarify public participation or industry self-regulation mechanisms (such as open source communities, platform self review committees), and the supervision of third-party independent audit institutions, it can fully unleash the potential of multi-party governance in society. By providing procedural guarantees for external supervision and feedback at the legislative level, it can not only reduce the burden of government single regulation, but also enable adaptive regulation to better absorb the requirements of "bottom-up" institutional adjustment.
Fourth, streamline and focus: avoid excessive coverage of articles or overlapping responsibilities. The legislative research on artificial intelligence has been influenced by the legislative ideas of the EU's Artificial Intelligence Law, and has generally attempted to solve many issues such as algorithm compliance, data sharing, personal information protection, and ethical review in a "package". This approach can lead to overlapping responsibility boundaries or regulatory overlaps, resulting in a heavy compliance burden in actual implementation. Adaptive regulation advocates establishing the bottom line responsibility for the most core risks in the early stages of legislation, while other risk regulations can be regulated through more specific and decentralized provisions, which can be refined by secondary regulations, departmental rules, or local legislative pilot projects. This can ensure the prominent focus of legislation and reserve space for the "rolling revision" of the later system.
Fifth, establish legislative evaluation and revision procedures. Traditional legislation often lacks institutionalized "post legislative evaluation" procedures, making it difficult for the text itself to resonate with technological development. It is recommended that all laws and regulations related to the artificial intelligence industry add a "legislative effect evaluation clause" when formulating, clearly stipulating that cross departmental and interdisciplinary specialized evaluation meetings should be held regularly to quantitatively or qualitatively evaluate the application effect of the law, enterprise compliance costs, public feedback, and other aspects. Important defects or new issues discovered can be corrected through rapid revision procedures or by authorizing departmental regulations to refine provisions. This "lightweight and iterative" legislative update mechanism can precisely match the governance rhythm of the rapid evolution of the artificial intelligence industry.
4.3 How to Strengthen 'Behavioral Incentives': Balancing Risk Control and Innovation Promotion
Under the traditional legal governance model, legislators typically rely on "compliance obligations" tools to constrain corporate behavior through rigid means such as administrative licensing, approval, and punishment. Whether it is security prevention, innovative development, or rights protection, the core goal of artificial intelligence legislation is essentially closely related to how to adjust the behavior choices of enterprises and researchers. If companies rely solely on post punishment for illegal behavior, they will tend to adhere conservatively or passively to minimum compliance standards; If administrative approval is further strengthened or strict pre examination is carried out, it may damage the vitality of technological progress and business development. In comparison, the idea of behavioral incentives should be through a series of positive and negative incentives such as fiscal investment, tax regulation, credit rating, industrial support, administrative rewards, and targeted procurement. Through the effective combination of high power administration and new administrative measures, hard law and soft law, enterprises can voluntarily or actively consider social values and public ethics while pursuing economic benefits, thereby achieving the effect of "being willing to comply" rather than "having to comply". In other words, if the theoretical framework of behavioral law and economics is applied, the R&D and compliance decisions of enterprises are not entirely the product of rational calculations, but will be influenced by a series of cognitive biases, expected incentives, and institutional frameworks. Therefore, the design of policy tools needs to combine expected utility theory, prospect theory, and soft incentive (Nudging) mechanisms to reduce short-term compliance costs for enterprises while shaping long-term innovation incentive structures, so that market entities not only actively comply, but also are willing to invest in research and development to obtain higher market returns in the future.
Firstly, the establishment of a dynamic infringement liability mechanism. In traditional tort law, strict liability and fault liability are often used to regulate high-risk industries, but research in behavioral law and economics shows that these liability principles may lead to reverse incentives in practice, and even reduce the safety investment of enterprises. Strict liability may lead companies to choose to exit the market due to expected huge compensation, while fault liability may lead companies to adopt a "minimum compliance" strategy, only meeting the minimum standards required by law, without actively optimizing security design systems. Therefore, in the field of artificial intelligence governance, implementing adaptive regulation requires the introduction of a dynamic infringement liability mechanism, combined with economic incentives, risk pricing, and legal liability adjustments, to promote the endogenous improvement of safety standards in enterprises. By combining dynamic infringement liability mechanisms with market incentive tools, artificial intelligence regulation can promote enterprises to continuously optimize safety without weakening innovation motivation, forming a self strengthening mechanism of "compliance competition optimization". This approach not only reduces excessive regulation caused by moral intuition, but also effectively fills the gaps in traditional legal liability systems in high-tech risk governance.
Secondly, the optimization of data ownership concept: enabling enterprises to actively protect user rights and interests. Firstly, in terms of sharing personal data benefits, traditional data protection legal frameworks mainly focus on protecting users' privacy rights. Research in behavioral law and economics shows that users' perception of rights is closely related to their ability to obtain benefits. When users believe that their data has significant value to the enterprise but cannot receive corresponding returns, it may reduce their trust and willingness to cooperate with the platform. Therefore, the government can promote incentive mechanisms for data revenue distribution, encouraging companies to provide users with certain economic returns in compliant data trading or data analysis activities. For example, companies can adopt a "data dividend plan", where users can receive points, discounts, or direct economic benefits when they agree to the platform's use of their data, thereby enhancing their enthusiasm for data sharing. In addition, the government can introduce a "dynamic data pricing mechanism" that allows users to set different levels of authorization based on data sensitivity, personal preferences, and usage scenarios, enabling users who provide high-value data to receive higher compensation. This incentive mechanism not only enhances users' control over their own data, but also improves the transparency of enterprise data governance, ultimately prompting platforms to pay more attention to protecting user rights in market competition.
Thirdly, regarding the open sharing of intellectual property rights. The rapid development of artificial intelligence technology requires the widespread circulation of data and algorithms. Overly closed intellectual property protection may hinder technology diffusion and even form market monopolies. Behavioral law and economics emphasize that refined property rights structure design and moderate compulsory licensing system can avoid excessive protection of data or technological resources by market entities due to the "psychological ownership effect", thereby promoting knowledge circulation and market competition. The government can promote incentive compatible intellectual property sharing mechanisms, that is, in key technology areas that are highly relevant to public interests or urgently needed by society (such as smart healthcare, public transportation, etc.), a controllable data openness system can be established for enterprises that receive government funding, requiring them to open up some training data or non core algorithm models when enjoying policy support (such as tax incentives or government procurement), in order to promote overall technological progress in the industry. In addition, the government can establish a dynamic mandatory licensing system, combined with prospect theory, to set non exclusive usage rights or data sharing requirements for enterprises after obtaining high market returns, ensuring that key technologies can benefit society more widely. This progressive licensing model helps reduce companies' resistance to mandatory technology sharing and avoids excessive restrictions on technological innovation.
Fourthly, regarding fiscal incentives. Traditional tax incentives and subsidy policies are often based on the assumption of complete rationality of enterprises, assuming that they will automatically increase their R&D investment driven by tax incentives. However, research in behavioral law and economics has shown that the discount rate of long-term returns by corporate management is often too high, resulting in a "short-sighted bias" that leads companies to prefer investment directions with higher short-term returns, while ignoring basic research and development that has high upfront costs, long cycles, but more substantial long-term returns. Therefore, the government can introduce a dynamic tax incentive mechanism, which provides incremental tax reductions for enterprises that continue to invest in compliant research and development. For example, if a company meets ethical standards for artificial intelligence and invests in safety research and development for five consecutive years, the applicable tax incentives will gradually increase. This design utilizes the loss avoidance effect, allowing companies to maintain high compliance standards due to the fear of losing accumulated tax benefits. In addition, to cope with high-risk and high investment ethical research and development in artificial intelligence, the government can also introduce a research and development risk sharing mechanism, establish a research and development failure compensation fund, and provide partial compensation for failure losses to enterprises that meet ethical and safety standards. This mechanism can correct the probability weighted psychological tendency of enterprise managers, which overestimates the likelihood of low probability failure, thereby reducing avoidance behavior towards high-risk and high cost projects.
Fifth, fairness and corporate innovation. In terms of market competition incentives, a company's perception of fairness often determines its compliance and innovation enthusiasm. If a company believes that regulatory policies are more lenient towards its competitors, it may choose to adhere to minimum compliance standards rather than proactively raising technical safety standards. Therefore, the government can establish a fair market competition environment through market access, industry ranking, public reputation disclosure, and other means to incentivize enterprises to find the optimal balance between compliance and innovation. For example, the government can use signal transmission theory and refer to the "soft incentive" theory to launch the "ethical technology" brand certification, establish the "ethical technology certification mark" for artificial intelligence products that meet high ethical standards, and provide competitive incentives in government procurement, market access, tax incentives, and other aspects. This can not only reduce the compliance costs of enterprises, but also make ethical standards a value-added element in market competition, rather than a simple compliance obligation. In addition, in the public procurement and market access process, the government can adopt the method of competitive compliance bidding, which requires companies to submit their compliance plans in terms of fairness, security, data protection, etc. during the bidding process, and give additional points in the scoring mechanism. This type of mechanism can encourage companies to actively optimize their compliance level through market competition pressure, rather than just passively accepting regulation.
Sixth, a competitive and fair incentive mechanism. In terms of algorithmic fairness incentives, traditional legal liability systems often adopt a "strict liability" or "fault liability" model, which means that if a company makes discriminatory decisions or damages user interests due to algorithmic bias, it must bear legal responsibility. However, research in behavioral law and economics has found that a simple punishment mechanism may trigger "negative compliance" behavior in companies, where companies only meet the minimum legal requirements and do not actively optimize algorithms to reduce social risks. Therefore, the government can use a "competitive fairness incentive mechanism", which includes establishing fair algorithm certification, government subsidies, award incentives, and other methods to enable enterprises to obtain actual benefits from optimizing algorithm fairness in market competition. For example, the government can establish a "Fair Technology Award" to encourage companies to voluntarily participate in algorithm fairness evaluations, and provide policy support to companies that perform well, such as tax exemptions, government procurement priorities, etc. In addition, a "differentiated supervision" strategy can be adopted, which means that for enterprises that can actively disclose algorithm transparency and accept fairness review, the frequency of administrative review or compliance costs can be reduced, and "high transparency and high fairness" enterprises can enjoy the advantage of market competition.
5. Conclusion: The Future of Adaptive Law from the Perspective of Chinese Rule of Law
There are still many issues in this article that have not been fully explored or require further research.
Firstly, the cross-border nature of artificial intelligence determines that any legislative attempt cannot be limited to a closed context of one country or region. How to achieve coordination and coupling of adaptive legislation in a multilateral context, especially in the face of complex issues such as international data flow, algorithm output control, and "digital sovereignty", still relies on a more comprehensive international legal perspective and cross-border mechanism design.
Secondly, as cutting-edge technologies such as brain computer interfaces and multimodal generative artificial intelligence gradually enter the implementation stage, technological uncertainty and ethical risks may be further amplified. Whether existing adaptive regulatory tools are equally effective in dealing with large-scale or high-risk scenarios requires continuous dynamic evaluation and empirical testing.
Again, the "behavioral incentive" model emphasized in this article only provides a preliminary outline of the basic framework of "incentive feedback revision". The improvement of the system still requires more empirical cases and interdisciplinary cooperation to demonstrate the effectiveness, operability, and possible impact of specific incentive measures on social equity; How to incorporate the risk game between the government and platform enterprises into this framework to prevent corporate "policy arbitrage" is also an important issue worthy of further institutional refinement. In addition, from the perspective of national governance, the success of adaptive rule of law also depends on the flexibility of political and administrative systems, the cautious attitude of the judiciary towards new technological risks, and the tolerance of society towards the potential benefits and negative externalities that may arise during the trial and error process. If there is a lack of macro level institutional cooperation and the pursuit of adaptability solely from the perspective of legal provisions, it is inevitable to fall into the awkward situation of "advanced concepts and weak implementation". It should be pointed out that this legal paradigm that emphasizes dynamic optimization and open governance does not deny the foundational role of legal doctrine in artificial intelligence legislation. However, in terms of specific rule revision and guidance mechanisms, interdisciplinary methods such as empirical research, legal economics, and technical ethics assessment need to be combined to ensure that legislation meets local practical needs while maintaining sufficient adaptability in a rapidly evolving technological environment.
Finally, in the context of a new round of global technological competition and industrial transformation, transforming the institutional advantages inherent in the traditional "experiment promotion" approach of Chinese style rule of law into a governance paradigm with universal value relies on continuous theoretical innovation and practical promotion. If more detailed cases can be accumulated in international exchanges, local legislation, and corporate compliance innovation in the future, and interdisciplinary dialogues can be conducted with fields such as complex science, systems engineering, sociology, etc., it is expected to provide a more solid practical foundation for adaptive rule of law theory and contribute more inspiring "Chinese solutions" to global artificial intelligence governance.