IPR Daily's Exclusive Dialogue with Professor Daryl Lim

Source:IP Today

During the 2025 BIP ASIA Forum in Hongkong, Camila Li, Head of the International Business Department at IPR Daily, conducted an exclusive academic dialogue with Professor Daryl Lim, Associate Dean for Research and Strategic Partnerships at Penn State Dickinson Law, Founder and Director of the Intellectual Property and Innovation Initiative, on cutting-edge issues concerning intellectual property (IP) and artificial intelligence (AI).

Professor Daryl Lim has long been engaged in interdisciplinary research on intellectual property law, antitrust law, data science, and AI legal regulation, with fruitful academic achievements. He recently won the "IP Professor of the Year Award" and holds core research responsibilities in multiple AI research centers at the university. This interview focused on key propositions such as the interdisciplinary training of legal talents, the dynamic balance between intellectual property and antitrust, the institutional construction of responsible AI, the participation paths of developing countries in global IP rules, and data compliance. Combining his academic research and practical experience, Professor Daryl Lim put forward academic insights with theoretical depth and application value.

The Construction of Interdisciplinary Competences for Legal Talents: Dual Dimensions of Technical Literacy and Ethical Cognition

IPR Daily: Professor Daryl Lim, thank you for accepting this academic dialogue with IPR Daily. First of all, could you please introduce your academic background and research directions to our readers in the academic and practical fields of intellectual property in China?

Professor Daryl Lim: Hello to all. I am currently the Associate Dean for Research and Strategic Partnerships at Penn State Dickinson Law, and also the Founder and Director of the Intellectual Property and Innovation Initiative at the law school. At the university level, I participate in the core work of multiple artificial intelligence research centers. My core research areas focus on the interdisciplinary integration of law, data science, and artificial intelligence, especially forming a series of research results on the synergistic mechanism between intellectual property law, antitrust law, and AI regulation.

IPR Daily: Your research has always been based on an interdisciplinary perspective, and your academic achievements have been recognized by the international academic community. Against the academic background of the deep integration of intellectual property with AI and data technologies, what interdisciplinary competences do you think law students should prioritize developing in addition to basic legal knowledge to meet the needs of industry development?

Professor Daryl Lim: Firstly, technical literacy . Law students do not need to reach the technical depth of computer science majors, but an understanding of how technology and law works would be beneficial . Secondly, ethical literacy .  would help lawyers give ethically sound advice in this fast-moving space. 

Third,, the ability to acquire and apply interdisiciplinary knowledge across fields is crucial. The traditional legal knowledge system in a single field can no longer respond to complex technical and legal issues, and the cross-application of intellectual property law, competition law, and privacy law has become the norm. Therefore, law students need to build a "problem-oriented" knowledge integration system and improve their ability to design cross-domain legal solutions.

Synergy Between Intellectual Property and Antitrust: A Dynamic Balance Mechanism for Innovation Incentives and Market Competition

IPR Daily: There is an inherent tension between the exclusivity of intellectual property and the competitiveness of antitrust law. As a senior scholar in both fields, how do you think enterprises should build a compliance system that balances innovation incentives and market competition?

Professor Daryl Lim: The balance between the two is essentially a dynamic synergistic relationship. Intellectual property law provides an incentive to innovate by granting right holders limited-term exclusive rights; while antitrust law defines the boundaries of right exercise to prevent market distortions caused by abuse of rights. The complementarity of these institutional functions determines that balance is not a static threshold, but a process that needs to be dynamically adjusted in based on a case-by-case basis .

The Institutional Construction of Responsible AI: With Justice, Equity, and the Rule of Law as Core Pillars

IPR Daily: You have deeply participated in the construction of international rules for the deployment of responsible AI, especially achieving remarkable results in the field of synergy between intellectual property and data governance. Could you share the core academic viewpoints and institutional design ideas in this field?

Professor Daryl Lim: In my recent article "Legal Regulation of Socially Responsible Artificial Intelligence" published in the Duke Law & Technology Review, I proposed three core pillars for the deployment of responsible AI — justice, equity, and the rule of law — which constitute the theoretical framework and practical path for technical regulation.

The core of the justice dimension is to improve the accessibility of the legal system. AI technology provides technical support for legal inclusion. For example, using remote video technology to enable groups in remote areas to obtain professional legal services; eliminating language barriers with real-time translation systems to protect the procedural participation rights of non-native speakers and achieve the balanced allocation of legal resources.

The equity dimension focuses on the design of algorithms. In the process of large language model training and algorithm development, it is necessary to establish a regular fairness audit mechanism to identify and eliminate implicit biases such as race and gender contained in training data. Ignoring the transmission effect of data biases may lead to discriminatory consequences of algorithmic decisions, which fundamentally conflicts with the equal value of law and must be prevented through dual technical and institutional means.

The rule of law dimension requires building a legal system that balances commercial innovation and social value. This system needs to achieve three goals: providing enterprises with clear compliance expectations, conforming to the value demands of the public, and ensuring the positive social effects of technological development, forming a positive interaction between technological innovation and institutional regulation.

The Participation of Developing Countries in Global IP Rules: Path Optimization Based on Comparative Advantages

IPR Daily: Combining your global consulting experience, from a comparative law perspective, how can developing countries enhance their right to speak in the formulation of global intellectual property rules and balance innovation interests and development rights?

Professor Daryl Lim:  The global trade and intellectual property system has set flexible provisions for this purpose, such as the grace period for fulfilling obligations under the WTO framework. Developing countries may use those flexibilities to build an intellectual property system compatible with their own development stage to develop economically.

Secondly, alliance-based cooperation may be used to enhance bargaining power. A single developing country has limited market size and political influence, but the practice of cooperation mechanisms such as the " Forum of Small States" has shown that through coordination , the collective bargaining power of developing countries can be effectively enhanced. 

Future Changes in the IP Field: Institutional Challenges and Privacy Protection Driven by AI

IPR Daily: Looking ahead to the next 3 to 5 years, what disruptive impacts do you think AI technology will have on the intellectual property field? What challenges will these changes pose to the protection of the rights of the general public?

Professor Daryl Lim: The popularization of technologies such as generative AI has triggered systemic challenges to the intellectual property system, mainly reflected in three major issues: first, the determination of copyright ownership of content generated by large language models; second, the infringement determination standards in AI training and application; third, the definition of secondary infringement liability in cross-border scenarios. Global judicial institutions are gradually exploring solutions through typical cases, but these preliminary conclusions have derived new legal interpretation issues, forming a dynamic cycle of "problem-response-new problem".

A recent case heard by a UK court highlighted the complexity of cross-border liability: if a large language model is trained in Country A and commercially deployed in Country B, what connecting factor should be used to determine the infringement liability of its developer? Such issues will become a core issue in cross-border intellectual property disputes in the future.

For the general public, the core impacts are reflected in two aspects: first, the increased risk of privacy dilution. The popularization of AI technology makes the collection, analysis, and utilization of personal information more convenient, and the traditional privacy protection system faces structural challenges; second, the uncertainty in expected norms and rules caused by insufficient global rule consensus. Countries need to seek a balance between their own development constraints and international cooperation. This process is similar to the "shared ship" effect in global governance. As stakeholders, more than 190 countries need to build consensus while respecting differences, because the governance issues of AI and intellectual property are essentially common challenges faced by all mankind, and only through collaborative cooperation can win-win results be achieved.

Data Commercialization and Compliance: An Institutional Path of Source Governance and Government-Enterprise Collaboration

IPR Daily: In the practice of data commercialization, what legal obstacles do enterprises often face? What is the weight of data copyright definition and privacy compliance in this process?

Professor Daryl Lim: The manifestations of legal obstacles are highly related to the size and business type of enterprises. For small and medium-sized enterprises (SMEs), the core problem lies in the lack of compliance awareness, that is, failing to establish a pre-compliance mechanism in the data collection and use links, leading to subsequent regulatory penalties, resulting in the dual risks of soaring compliance costs and business interruptions. This is the most prominent problem in current data commercialization practices.

The key to solving this problem is to build a compliance governance system with government-enterprise collaboration. Regulatory authorities should establish a regular compliance guidance mechanism, promote the popularization of the "Compliance by Design" concept, and help enterprises integrate compliance elements in the product R&D and business model design stages. This public-private partnership model needs to be continuously optimized with technological iteration to form a dynamically adaptive data compliance governance framework.

IPR Daily: For SMEs planning to enter the field of data commercialization, what are the compliance "red lines" they are most likely to cross? How should they build a systematic risk prevention mechanism?

Professor Daryl Lim: The core risk point is the lack of a compliance culture, that is, failing to integrate compliance requirements into the entire business process, forming a passive pattern of "remedial measures after the event" rather than "prevention in advance". Building a risk prevention mechanism requires breakthroughs in two dimensions: first, embedding compliance concepts into the source of product design and business model construction, and establishing full-process compliance review nodes; second, strengthening active engagement with regulatory authorities, and improving the efficiency and accuracy of their own compliance capacity building with the help of official guidance and public service resources.

The Rule Construction of the Data Market: Dual Gaps in Right Definition and Value Evaluation

IPR Daily: Combining your dual experience in academic research and global consulting, what are the most urgent rule gaps to be filled in building a dynamically safe data market?

Professor Daryl Lim: Rights and obligations should be clear. Like traffic rules such as "stop at red lights and go at green lights", they should have clarity and enforceability to ensure that the rights and obligations of all subjects are clearly defined.

Second, the lack of a data value evaluation system. As a new type of intangible asset, the theory and method of data value evaluation are still in the exploration stage. Compared with the intellectual property value evaluation system that has developed for more than 400 years, the definition of data value lacks mature standards. Therefore, it is necessary to build a scientific and reasonable data value evaluation framework through the combination of industry dialogue, academic research, and practical pilots, which is the basic institutional guarantee for the healthy development of the data market. The mature development of the data market requires continuous interdisciplinary communication and collaborative innovation, and this interview is also an important part of this process.

IPR Daily: Thank you again for Professor Lim's in-depth sharing. Your academic insights have provided important enlightenment for the research and practice in the fields of intellectual property and AI governance in China.

Professor Daryl Lim: Thank you to IPR Daily for providing this exchange platform. I hope to help bring stakeholders in China and the U.S. together and foster collaboration between the two sides. 

Source:IP Today