The human genes database or human genome project, the product under the policy of biotechnology no matter in a developed or developing country, has been paid more attention by a government and an ordinary people gradually. The construction of human genes database or human genome project, which is not only related to a country’s innovation on biotechnology, but also concerns the promotion of a country’s medical quality, the construction of medical care system, and the advantages brought by the usage of bio-information stored in human genes database or from human genome project. However, even though every country has a high interest in setting up human genes database or performing human genome project, the issues concerning the purposes of related biotechnology policies, the distribution of advantages and risks and the management of bio-information, since each country has different recognition upon human genes database or human genome project and has varied standards of protecting human basic rights, there would be a totally difference upon planning biotechnology policies or forming the related systems. Right now, the countries that vigorously discuss human genes database or practice human genome project include England, Iceland, Norway, Sweden, Latvia and Estonia.
Estonia, which is the country around the Baltic Sea, has planned to set up its own human genes database in order to draw attention from other advanced countries, to attract intelligent international researchers or research groups, and to be in the lead in the area of biotechnology. To sum up, the purpose of constructing Estonian human genes database was to collect the genes and health information of nearly 70% Estonia’s population and to encourage bio-research and promote medical quality.
The construction of Estonian human genes database started from Estonian Genome Project (EGP). This project was advocated by the professor of biotechnology Andres Metspalu at Tartu University in Estonia, and he proposed the idea of setting up Estonian human genes database in 1999. The purposes of EGP not only tried to make the economy of Estonia shift from low-cost manufacturing and heavy industry to an advanced technological economy, but also attempted to draw other countries’ attention and to increase the opportunity of making international bio-researches, and then promoted the development of biotechnology and assisted in building the system of medical care in Estonia.
EGP started from the agreement made between Estonian government and Eesti Geenikeskus (Estonian Genome Foundation) in March, 1999. Estonian Genome Foundation was a non-profit organization formed by Estonian scientists, doctors and politicians, and its original purposes were to support genes researches, assist in proceeding any project of biotechnology and to set up EGP. The original goals of constructing EGP were “(a) reaching a new level in health care, reduction of costs, and more effective health care, (b) improving knowledge of individuals, genotype-based risk assessment and preventive medicine, and helping the next generation, (c) increasing competitiveness of Estonia – developing infrastructure, investments into high-technology, well-paid jobs, and science intensive products and services, (d) [constructing] better management of health databases (phenotype/genotype database), (e) … [supporting]… economic development through improving gene technology that opens cooperation possibilities and creates synergy between different fields (e.g., gene technology, IT, agriculture, health care)”1.
In order to ensure that Estonian human genes database could be operated properly and reasonably in the perspectives of law, ethics and society in Estonia, the Estonian parliament followed the step of Iceland to enact “Human Genes Research Act” (HGRA) via a special legislative process to regulate its human genes database in 2000. HGRA not only authorizes the chief processor to manage Estonian human genes database, but also regulates the issues with regard to the procedure of donation, the maintenance and building of human genes database, the organization of making researches, the confidential identity of donator or patient, the discrimination of genes, and so on.
Since the construction of Estonian human genes database might bring the conflicts of different points of view upon the database in Estonia, in order to “avoid fragmentation of societal solidarity and ensure public acceptability and respectability”2 , HGRA adopted international standards regulating a genes research to be a norm of maintaining and building the database. Those standards include UNESCO Universal Declaration on the Human Genome and Human Rights (1997) and the Council of Europe’s Convention on Human Rights and Biomedicine (1997).
The purpose of enacting HGRA is mainly to encourage and promote genes researches in Estonia via building Estonian human genes database. By means of utilizing the bio-information stored in the database, it can generate “more exact and efficient drug development, new diagnostic tests, improved individualized treatment and determination of risks of the development of a disease in the future”3 . In order to achieve the above objectives, HGRA primarily puts emphasis on several aspects. Those aspects include providing stronger protection on confidential identity of donators or patients, caring for their privacy, ensuring their autonomy to make donations, and avoiding any possibility that discrimination may happen because of the disclosure of donators’ or patients’ genes information.
1.HERBERT GOTTWEIS & ALAN PETERSEN, BIOBANKS – GOVERNANCE IN COMPARATIVE PERSPECTIVE 59 (2008).
2.Andres Rannamae, Populations and Genetics – Legal and Socio-Ethical Perspectives, in Estonian Genome Porject – Large Scale Health Status Description and DNA Collection 18, 21 (Bartha Maria Knoppers et al. eds., 2003.
3.REMIGIUS N. NWABUEZE, BIOTECHNOLOGY AND THE CHALLENGE OF PROPERTY – PROPERTY RIGHTS IN DEAD BODIES, BODY PARTS, AND GENETIC INFORMATION, 163 (2007).
Draft of AI Product and System Evaluation Guidelines Released by the Administration for Digital Industries to Enhance AI Governance 2024/08/15 I. AI Taiwan Action Plan 2.0 In 2018, the Executive Yuan launched the “AI Taiwan Action Plan” to ensure that the country keeps pace with AI developments. This strategic initiative focuses on attracting top talent, advancing research and development, and integrating AI into critical sectors such as smart manufacturing and healthcare. The action plan has sparked growing discussion on AI regulation. Through these efforts, Taiwan aims to position itself as a frontrunner in the global smart technology landscape. Later in 2023, the Executive Yuan updated the action plan, releasing “AI Taiwan Action Plan 2.0” to further strengthen AI development. “AI Taiwan Action Plan 2.0” outlines five main pillars: 1. Talent Development: Enhancing the quality and quantity of AI expertise, while improving public AI literacy through targeted education and training initiatives. 2. Technological and Industrial Advancement: Focusing on critical AI technologies and applications to foster industrial growth; and creating the Trustworthy AI Dialogue Engine (TAIDE) that communicates in Traditional Chinese. 3. Enhancing work environments: Establishing robust AI governance infrastructure to facilitate industry and governmental regulation, and to foster compliance with international standards. 4. International Collaboration: Expanding Taiwan's role in international AI forums, such as the Global Partnership on AI, to collaborate on developing trustworthy AI practices. 5. Societal and Humanitarian Engagement: Utilizing AI to tackle pressing societal challenges such as labor shortages, an aging population, and environmental sustainability. II. AI Product and System Evaluation Guidelines: A Risk-based Approach to AI Governance To support infrastructure, in March 2024, the Administration for Digital Industries issued the draft AI Product and System Evaluation Guidelines. The Guidelines are intended to serve as a reference for industry when developing and using AI products and systems, thus laying a crucial foundation for advancing AI-related policies in Taiwan. The Guidelines outline several potential risks associated with AI: 1. Third-Party Software and Hardware: While third-party software, hardware, and datasets can accelerate development, they may also introduce risks into AI products and systems. Therefore, effective risk management policies are crucial. 2. System Transparency: The lack of transparency in AI products and systems makes risk assessment relatively challenging. Inadequate transparency in AI models and datasets also pose risks for development and deployment. 3. Differences in Risk Perception: Developers of AI products and systems may overlook risks specific to different application scenarios. Moreover, risks may gradually emerge as the product or system is used and trained over time. 4. Application Domain Risks: Variations between testing results and actual operational performance can lead to differing risk assessments for evaluated products and systems. 5. Deviation from Human Behavioral Norms: If AI products and systems behave unexpectedly compared to human operations, this can indicate a drift in the product, system, or model, thereby introducing risks. The Guidelines also specify that businesses have to categorize risks when developing or using AI products and systems, and manage them in accordance with these classifications. In alignment with the EU AI Act, risks are classified into four levels: unacceptable, high, limited, and minimal. 1. Unacceptable Risk: If AI systems used by public or private entities provide social scoring of individuals, this could lead to discriminatory outcomes and the exclusion of certain groups. Furthermore, if AI systems are employed to manipulate the cognitive behavior of individuals or vulnerable populations, causing physical or psychological harm, such systems are deemed unacceptable and prohibited. 2. High risk: AI systems are classified as high-risk in several situations. These include applications used in critical infrastructure, such as transportation, where there is potential risk to citizens' safety and health. These situations also encompass AI systems involved in educational or vocational training (such as exam scoring), which can determine access to education or professional paths. AI used as safety-critical product components, such as robot-assisted surgery, also falls into this category. In the employment sector, AI systems used for managing recruitment processes, including CV-sorting software, are considered high-risk. Essential private and public services, such as credit scoring systems that impact loan eligibility, also fall under high-risk. AI used in law enforcement in ways that it may affect fundamental rights, such as evaluating the reliability of evidence, is also included. AI systems involved in migration, asylum, and border control, such as automated visa application examinations, are categorized as high-risk. Finally, AI solutions used in the administration of justice and democratic processes, such as court ruling searches, are also classified as high-risk. If an AI system is classified as high risk, it must be evaluated across ten criteria—Safety, Explainability, Resilience, Fairness, Accuracy, Transparency, Accountability, Reliability, Privacy, and Security—to ensure the AI system’s quality. 3. Limited risk: When an AI product or system is classified as having limited risk, it is up to the enterprise to determine whether an evaluation is required. The Guidelines also introduce specific transparency obligations to ensure that humans are informed when necessary, thus fostering trust. For instance, when using AI systems such as chatbots or systems for generating deepfake content, humans must be made aware that they are interacting with a machine so they can take an informed decision to continue or step back. 4. Minimal or no risk: The Guidelines allow the free use of minimal-risk AI. This includes applications such as AI-enabled video games and spam filters. Ⅲ. Conclusion The AI Product and System Evaluation Guidelines represent a significant step forward in establishing a robust, risk-based framework for AI governance in Taiwan. By aligning with international standards like the EU AI Act, these Guidelines ensure that AI products and systems are rigorously assessed and categorized into four distinct risk levels: unacceptable, high, limited, and minimal. This structured approach allows businesses to manage AI-related risks more effectively, ensuring that systems are safe, transparent, and accountable. The emphasis on evaluating AI systems across ten critical criteria—including safety, explainability, and fairness—reflects a comprehensive strategy to mitigate potential risks. This proactive approach not only safeguards the public but also fosters trust in AI technologies. By setting clear expectations and responsibilities for businesses, the Guidelines promote responsible development and deployment of AI, ultimately contributing to Taiwan's goal of becoming a leader in the global AI landscape.
Blockchain and General Data Protection Regulation (GDPR) compliance issues (2019)Blockchain and General Data Protection Regulation (GDPR) compliance issues (2019) I. Brief Blockchain technology can solve the problem of trust between data demanders and data providers. In other words, in a centralized mode, data demanders can only choose to believe that the centralized platform will not contain the false information. However, in the decentralized mode, data isn’t controlled by one individual group or organization[1], data demanders can directly verify information such as data source, time, and authorization on the blockchain without worrying about the correctness and authenticity of the data. Take the “immutable” for example, it is conflict with the right to erase (also known as the right to be forgotten) in the GDPR.With encryption and one-time pad (OTP) technology, data subjects can make data off-chain storaged or modified at any time in a decentralized platform, so the problem that data on blockchain not meet the GDPR regulation has gradually faded away. II. What is GDPR? The purpose of the EU GDPR is to protect user’s data and to prevent large-scale online platforms or large enterprises from collecting or using user’s data without their permission. Violators will be punished by the EU with up to 20 million Euros (equal to 700 million NT dollars) or 4% of the worldwide annual revenue of the prior financial year. The aim is to promote free movement of personal data within the European Union, while maintaining adequate level of data protection. It is a technology-neutral law, any type of technology which is for processing personal data is applicable. So problem about whether the data on blockchain fits GDPR regulation has raise. Since the blockchain is decentralized, one of the original design goals is to avoid a large amount of centralized data being abused. Blockchain can be divided into permissioned blockchains and permissionless blockchains. The former can also be called “private chains” or “alliance chains” or “enterprise chains”, that means no one can join the blockchain without consent. The latter can also be called “public chains”, which means that anyone can participate on chain without obtaining consent. Sometimes, private chain is not completely decentralized. The demand for the use of blockchain has developed a hybrid of two types of blockchain, called “alliance chain”, which not only maintains the privacy of the private chain, but also maintains the characteristics of public chains. The information on the alliance chain will be open and transparent, and it is in conflict with the application of GDPR. III. How to GDPR apply to blockchain ? First, it should be determined whether the data on the blockchain is personal data protected by GDPR. Second, what is the relationship and respective responsibilities of the data subject, data controller, and data processor? Finally, we discuss the common technical characteristics of blockchain and how it is applicable to GDPR. 1. Data on the blockchain is personal data protected by GDPR? First of all, starting from the technical characteristics of the blockchain, blockchain technology is commonly decentralized, anonymous, immutable, trackable and encrypted. The other five major characteristics are immutability, authenticity, transparency, uniqueness, and collective consensus. Further, the blockchain is an open, decentralized ledger technology that can effectively verify and permanently store transactions between two parties, and can be proved. It is a distributed database, all users on the chain can access to the database and the history record, also can directly verify transaction records. Each nodes use peer-to-peer transmission for upload or transfer information without third-party intermediation, which is the unique “decentralization” feature of the blockchain. In addition, the node or any user on the chain has a unique and identifiable set of more than 30 alphanumeric addresses, but the user may choose to be anonymous or provide identification, which is also a feature of transparency with pseudonymity[2]; Data on blockchain is irreversibility of records. Once the transaction is recorded and updated on the chain, it is difficult to change and is permanently stored in the database, that is to say, it has the characteristics of “tamper-resistance”[3]. According to Article 4 (1) of the GDPR, “personal data” means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person. Therefore, if data subject cannot be identified by the personal data on the blockchain, that is an anonymous data, excluding the application of GDPR. (1) What is Anonymization? According to Opinion 05/2014 on Anonymization Techniques by Article 29 Data Protection Working Party of the European Union, “anonymization” is a technique applied to personal data in order to achieve irreversible de-identification[4]. And it also said the “Hash function” of blockchain is a pseudonymization technology, the personal data is possible to be re-identified. Therefore it’s not an “anonymization”, the data on the blockchain may still be the personal data stipulated by the GDPR. As the blockchain evolves, it will be possible to develop technologies that are not regulated by GDPR, such as part of the encryption process, which will be able to pass the court or European data protection authorities requirement of anonymization. There are also many compliance solutions which use technical in the industry, such as avoiding transaction data stored directly on the chain. 2. International data transmission Furthermore, in accordance with Article 3 of the GDPR, “This Regulation applies to the processing of personal data in the context of the activities of an establishment of a controller or a processor in the Union, regardless of whether the processing takes place in the Union or not. This Regulation applies to the processing of personal data of data subjects who are in the Union by a controller or processor not established in the Union, where the processing activities are related to: (a) the offering of goods or services, irrespective of whether a payment of the data subject is required, to such data subjects in the Union; or (b) the monitoring of their behaviour as far as their behaviour takes place within the Union”.[5] In other words, GDPR applies only when the data on the blockchain is not anonymized, and involves the processing of personal data of EU citizens. 3. Identification of data controllers and data processors Therefore, if the encryption technology involves the public storage of EU citizens' personal data and passes it to a third-party controller, it may be identified as the “data controller” under Article 4 of GDPR, and all nodes and miners of the platform may be deemed as the “co-controller” of the data, and be assumed joint responsibility with the data controller by GDPR. For example, the parties can claim the right to delete data from the data controller. In addition, a blockchain operator may be identified as a “processor”, for example, Backend as a Service (BaaS) products, the third parties provide network infrastructure for users, and let users manage and store personal data. Such Cloud Services Companies provide online services on behalf of customers, do not act as “data controllers”. Some commentators believe that in the case of private chains or alliance chains, such as land records transmission, inter-bank customer information sharing, etc., compared to public chain applications: such as cryptocurrencies (Bitcoin for example), is not completely decentralized, and more likely to meet GDPR requirements[6]. For example, in the case of a private chain or alliance chain, it is a closed platform, which contains only a small number of trusted nodes, is more effective in complying with the GDPR rules. 4. Data subject claims In accordance with Article 17 of the GDPR, The data subject shall have the right to obtain from the controller the erasure of personal data concerning him or her without undue delay and the controller shall have the obligation to erase personal data without undue delay under some grounds. Off-chain storage technology can help the blockchain industry comply with GDPR rules, allowing offline storage of personal data, or allow trusted nodes to delete the private key of encrypted information, which leaving data that cannot be read and identified on the chain. If the data is in accordance with the definition of anonymization by GDPR, there is no room for GDPR to be applied. IV. Conclusion In summary, it’s seem that the application of blockchain to GDPR may include: (a) being difficulty to identified the data controllers and data processors after the data subject upload their data. (b) the nature of decentralized storage is transnational storage, and Whether the country where the node is located, is meets the “adequacy decision” of Article 45 of the GDPR. If it cannot be met, then it needs to consider whether it conforms to the transfers subject to appropriate safeguards of Article 46, or the derogations for specific situations of Article 49 of the GDPR. Reference: [1] How to Trade Cryptocurrency: A Guide for (Future) Millionaires, https://wikijob.com/trading/cryptocurrency/how-to-trade-cryptocurrency [2] DONNA K. HAMMAKER, HEALTH RECORDS AND THE LAW 392 (5TH ED. 2018). [3] Iansiti, Marco, and Karim R. Lakhani, The Truth about Blockchain, Harvard Business Review 95, no. 1 (January-February 2017): 118-125, available at https://hbr.org/2017/01/the-truth-about-blockchain [4] Article 29 Data Protection Working Party, Opinion 05/2014 on Anonymisation Techniques (2014), https://www.pdpjournals.com/docs/88197.pdf [5] Directive 95/46/EC (General Data Protection Regulation), https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679&from=EN [6] Queen Mary University of London, Are blockchains compatible with data privacy law? https://www.qmul.ac.uk/media/news/2018/hss/are-blockchains-compatible-with-data-privacy-law.html
Impact of Government Organizational Reform to Research Legal System and Response Thereto (2) – Observation of the Swiss Research Innovation System3.Commission of Technology and Innovation (CTI) The CTI is also an institution dedicated to boosting innovation in Switzerland. Established in 1943, it was known as the Commission for the Promotion of Scientific Research[1]. It was initially established for the purpose of boosting economy and raising the employment rate, and renamed after 1996. The CTI and SNSF are two major entities dedicated to funding scientific research in Switzerland, and the difference between both resides in that the CTI is dedicated to funding R&D of the application technology and industrial technology helpful to Switzerland’s economic development. Upon enforcement of the amended RIPA 2011, the CTI was officially independent from the Federal Office for Professional Education and Technology (OEPT) and became an independent entity entitled to making decisions and subordinated to the Federal Department of Economic Affairs (FDEA) directly[2]. The CTI is subject to the council system, consisting of 65 professional members delegated from industrial, academic and research sectors. The members assume the office as a part time job. CTI members are entitled to making decisions on funding, utilization of resources and granting of CTI Start-up Label independently[3]. The CTI primarily carries out the mission including promotion of R&D of industrial technology, enhancement of the market-orientation innovation process and delivery of R&D energy into the market to boost industrial innovation. For innovation, the CTI's core mission is categorized into[4]: (1)Funding technology R&D activities with market potential The CTI invests considerable funds and resources in boosting the R&D of application technology and industrial technology. The CTI R&D Project is intended to fund private enterprises (particularly small-sized and medium-sized enterprises) to engage in R&D of innovation technology or product. The enterprises may propose their innovative ideas freely, and the CTI will decide whether the funds should be granted after assessing whether the ideas are innovative and potentially marketable[5]. CTI’s funding is conditioned on the industrial and academic cooperation. Therefore, the enterprises must work with at least one research institution (including a university, university of science and technology, or ETH) in the R&D. Considering that small-sized and medium-sized enterprises usually do not own enough working funds, technology and human resources to commercialize creative ideas, the CTI R&D Project is intended to resolve the problem about insufficient R&D energy and funds of small- and medium-sized enterprises by delivering the research institutions’ plentiful research energy and granting the private enterprises which work with research institutions (including university, university of science and technology, or ETH) the fund. Notably, CTI’s funding is applicable to R&D expenses only, e.g., research personnel’s salary and expenditure in equipment & materials, and allocated to the research institutions directly. Meanwhile, in order to enhance private enterprises' launch into R&D projects and make them liable for the R&D success or failure, CTI’s funding will be no more than 50% of the total R&D budget and, therefore, the enterprises are entitled to a high degree of control right in the process of R&D. The industrial types which the CTI R&D Project may apply to are not limited. Any innovative ideas with commercial potential may be proposed. For the time being, the key areas funded by CTI include the life science, engineering science, Nano technology and enabling sciences, etc.[6] It intends to keep Switzerland in the lead in these areas. As of 2011, in order to mitigate the impact of drastic CHF revaluation to the industries, the CTI launched its new R&D project, the CTI Voucher[7]. Given this, the CTI is not only an entity dedicated to funding but also plays an intermediary role in the industrial and academic sectors. Enterprises may submit proposals before finding any academic research institution partner. Upon preliminary examination of the proposals, the CTI will introduce competent academic research institutions to work with the enterprises in R&D, subject to the enterprises' R&D needs. After the cooperative partner is confirmed, CTI will grant the fund amounting to no more than CHF3,500,000 per application[8], provided that the funding shall be no more than 50% of the R&D project expenditure. The CTI R&D Project not only boosts innovation but also raises private enterprises’ willingness to participate in the academic and industrial cooperation, thereby narrowing the gap between the supply & demand of innovation R&D in the industrial and academic sectors. Notably, the Project has achieved remarkable effect in driving private enterprises’ investment in technology R&D. According to statistical data, in 2011, the CTI solicited additional investment of CHF1.3 from a private enterprise by investing each CHF1[9]. This is also one of the important reasons why the Swiss innovation system always acts vigorously. Table 1 2005-2011 Passing rate of application for R&D funding Year 2011 2010 2009 2008 2007 2006 2005 Quantity of applications 590 780 637 444 493 407 522 Quantity of funded applications 293 343 319 250 277 227 251 Pass rate 56% 44% 50% 56% 56% 56% 48% Data source: Prepared by the Study (2)Guiding high-tech start-up Switzerland has learnt that high-tech start-ups are critical to the creation of high-quality employment and boosting of economic growth, and start-ups were able to commercialize the R&D results. Therefore, as of 2001, Switzerland successively launched the CTI Entrepreneurship and CTI Startup to promote entrepreneurship and cultivate high-tech start-ups. 1.CTI Entrepreneurship The CTI Entrepreneurship was primarily implemented by the Venture Lab founded by CTI investment. The Venture Lab launched a series of entrepreneurship promotion and training courses, covering day workshops, five-day entrepreneurship intensive courses, and entrepreneurship courses available in universities. Each training course was reviewed by experts, and the experts would provide positive advice to attendants about innovative ideas and business models. Data source: Venture Lab Site Fig. 3 Venture Lab Startup Program 2.CTI Startup The CTI is dedicated to driving the economy by virtue of innovation as its priority mission. In order to cultivate the domestic start-ups with high growth potential in Switzerland, the CTI Startup project was launched in 1996[10] in order to provide entrepreneurs with the relevant guidance services. The project selected young entrepreneurs who provided innovative ideas, and guided them in the process of business start to work their innovative ideas and incorporate competitive start-ups. In order to enable the funding and resources to be utilized effectively, the CTI Startup project enrolled entrepreneurs under very strict procedure, which may be categorized into four stages[11]: Data source: CTI Startup Site Fig. 4 Startup Plan Flow Chart In the first stage, the CTI would preliminarily examine whether the applicant’s idea was innovative and whether it was technologically feasible, and help the applicant register with the CTI Startup project. Upon registration, a more concrete professional examination would be conducted at the second stage. The scope of examination included the technology, market, feasibility and management team’s competence. After that, at the stage of professional guidance, each team would be assigned a professional “entrepreneurship mentor”, who would help the team develop further and optimize the enterprise’s strategy, flow and business model in the process of business start, and provide guidance and advice on the concrete business issues encountered by the start-up. The stage of professional guidance was intended to guide start-ups to acquire the CTI Startup Label, as the CTI Startup Label was granted subject to very strict examination procedure. For example, in 2012, the CTI Startup project accepted 78 applications for entrepreneurship guidance, but finally the CTI Startup Label was granted to 27 applications only[12]. Since 1996, a total of 296 start-ups have acquired the CTI Startup Label, and more than 86% thereof are still operating now[13]. Apparently, the CTI Startup Label represents the certification for innovation and on-going development competence; therefore, it is more favored by investors at the stage of fund raising. Table 2 Execution of start-up plans for the latest three years Quantity of application Quantity of accepted application Quantity of CTI Label granted 2012 177 78 27 2011 160 80 26 2010 141 61 24 Data source: CTI Annual Report, prepared by the Study Meanwhile, the “CTI Invest” platform was established to help start-up raise funds at the very beginning to help commercialize R&D results and cross the valley in the process of R&D innovation. The platform is a private non-business-making organization, a high-tech start-up fund raising platform co-established by CTI and Swiss investors[14]. It is engaged in increasing exposure of the start-ups and contact with investors by organizing activities, in order to help the start-ups acquire investment funds. (3)Facilitating transfer of knowledge and technology between the academic sector and industrial sector KTT Support (Knowledge & Technology Transfer (KTT Support) is identified as another policy instrument dedicated to boosting innovation by the CTI. It is intended to facilitate the exchange of knowledge and technology between academic research institutions and private enterprises, in order to transfer and expand the innovation energy. As of 2013, the CTI has launched a brand new KTT Support project targeting at small-sized and medium-sized enterprises. The new KTT Support project consisted of three factors, including National Thematic Networks (NTNs), Innovation Mentors, and Physical and web-based platforms. Upon the CTI’s strict evaluation and consideration, a total of 8 cooperative innovation subjects were identified in 2012, namely, carbon fiber composite materials, design idea innovation, surface innovation, food study, Swiss biotechnology, wood innovation, photonics and logistics network, etc.[15] One NTN would be established per subject. The CTI would fund these NTNs to support the establishment of liaison channels and cooperative relations between academic research institutions and industries and provide small- and medium-sized enterprises in Switzerland with more rapid and easy channel to access technologies to promote the exchange of knowledge and technology between both parties. Innovation Mentors were professionals retained by the CTI, primarily responsible for evaluating the small-sized and medium-sized enterprises’ need and chance for innovation R&D and helping the enterprises solicit competent academic research partners to engage in the transfer of technology. The third factor of KTT Support, Physical and web-based platforms, is intended to help academic research institutions and private enterprises establish physical liaison channels through organization of activities and installation of network communication platforms, to enable the information about knowledge and technology transfer to be more transparent and communicable widely. In conclusion, the CTI has been dedicated to enhancing the link between scientific research and the industries and urging the industrial sector to involve and boost the R&D projects with market potential. The CTI’s business lines are all equipped with corresponding policy instruments to achieve the industrial-academic cooperation target and mitigate the gap between the industry and academic sectors in the innovation chain. The various CTI policy instruments may be applied in the following manner as identified in the following figure. Data source: CTI Annual Report 2011 Fig. 5 Application of CTI Policy Instrument to Innovation Chain III. Swiss Technology R&D Budget Management and Allocation The Swiss Federal Government has invested considerable expenditures in technology R&D. According to statistic data provided by Swiss Federal Statistical Office (FSO) and OECD, the Swiss research expenditures accounted for 2.37% of the Federal Government’s total expenditures, following the U.S.A. and South Korea (see Fig. 6). Meanwhile, the research expenditures of the Swiss Government grew from CHF2.777 billion in 2000 to CHF4.639 billion in 2010, an average yearly growth rate of 5.9% (see Fig. 7). It is clear that Switzerland highly values its technology R&D. Data source: FSO and OECD Fig. 6 Percentage of Research Expenditures in Various Country Governments’ Total Expenditures (2008) Data source: FSO and OECD Fig. 7 Swiss Government Research Expenditures 2000-2010 1.Management of Swiss Technology R&D Budget Swiss research expenditures are primarily allocated to the education, R&D and innovation areas, and play an important role in the Swiss innovation system. Therefore, a large part of the Swiss research expenditures are allocated to institutions of higher education, including ETH, universities, and UASs. The Swiss research expenditures are utilized by three hierarchies[16] (see Fig. 8): Government R&D funding agencies: The Swiss research budget is primarily executed by three agencies, including SERI, Federal Department of Economic Affairs, Education and Research, and Swiss Agency for Development and Cooperation (SDC). Intermediary R&D funding agencies: Including SNSC and CTI. Funding of R&D performing institutions: Including private enterprises, institutions of higher education and private non-profit-making business, et al. Therefore, the Swiss Government research expenditures may be utilized by the Federal Government directly, or assigned to intermediary agencies, which will allocate the same to the R&D performing institutions. SERI will allocate the research expenditures to institutions of higher education and also hand a lot of the expenditures over to SNSF for consolidated funding to the basic science of R&D. Data source: FSO Fig. 8 Swiss Research Fund Utilization Mechanism ~to be continued~ [1] ORGANIZATION FOR ECONNOMIC CO-OPERATION AND DEVELOPMENT [OECD], OECD Reviews of Innovation Policy: Switzerland 27 (2006). [2] As of January 1, 2013, the Federal Ministry of Economic Affairs was reorganized, and renamed into Federal Department of Economic Affairs, Education and Research (EAER). [3] The Commission for Technology and Innovation CTI, THE COMMISSION FOR TECHOLOGY AND INNOVATION CTI, http://www.kti.admin.ch/org/00079/index.html?lang=en (last visited Jun. 3, 2013). [4] Id. [5] CTI INVEST, Swiss Venture Guide 2012 (2012), at 44, http://www.cti-invest.ch/getattachment/7f901c03-0fe6-43b5-be47-6d05b6b84133/Full-Version.aspx (last visited Jun. 4, 2013). [6] CTI, CTI Activity Report 2012 14 (2013), available at http://www.kti.admin.ch/dokumentation/00077/index.html?lang=en&download=NHzLpZeg7t,lnp6I0NTU042l2Z6ln1ad1IZn4Z2qZpnO2Yuq2Z6gpJCDen16fmym162epYbg2c_JjKbNoKSn6A-- (last visited Jun. 3, 2013). [7] CTI Voucher, THE COMMISSION FOR TECHOLOGY AND INNOVATION CTI, http://www.kti.admin.ch/projektfoerderung/00025/00135/index.html?lang=en (last visited Jun. 3, 2013). [8] Id. [9] CTI, CTI Activity Report 2011 20 (2012), available at http://www.kti.admin.ch/dokumentation/00077/index.html?lang=en&download=NHzLpZeg7t,lnp6I0NTU042l2Z6ln1ad1IZn4Z2qZpnO2Yuq2Z6gpJCDeYR,gWym162epYbg2c_JjKbNoKSn6A--(last visited Jun. 3, 2013). [10] CTI Start-up Brings Science to Market, THE COMMISSION FOR TECHOLOGY AND INNOVATION CTI, http://www.ctistartup.ch/en/about/cti-start-/cti-start-up/ (last visited Jun. 5, 2013). [11] Id. [12] Supra note 8, at 45. [13] Id. [14] CTI Invest, http://www.cti-invest.ch/About/CTI-Invest.aspx (last visited Jun. 5, 2013). [15] KTT Support, CTI, http://www.kti.admin.ch/netzwerke/index.html?lang=en (last visited Jun.5, 2013). [16] Swiss Federal Statistics Office (SFO), Public Funding of Research in Switzerland 2000–2010 (2012), available at http://www.bfs.admin.ch/bfs/portal/en/index/themen/04/22/publ.Document.163273.pdf (last visited Jun. 20, 2013).
Hard Law or Soft Law? –Global AI Regulation Developments and Regulatory ConsiderationsHard Law or Soft Law? –Global AI Regulation Developments and Regulatory Considerations 2023/08/18 Since the launch of ChatGPT on November 30, 2022, the technology has been disrupting industries, shifting the way things used to work, bringing benefits but also problems. Several law suits were filed by artists, writers and voice actors in the US, claiming that the usage of copyright materials in training generative AI violates their copyright.[1] AI deepfake, hallucination and bias has also become the center of discussion, as the generation of fake news, false information, and biased decisions could deeply affect human rights and the society as a whole.[2] To retain the benefits of AI without causing damage to the society, regulators around the world have been accelerating their pace in establishing AI regulations. However, with the technology evolving at such speed and uncertainty, there is a lack of consensus on which regulation approach can effectively safeguard human rights while promoting innovation. This article will provide an overview of current AI regulation developments around the world, a preliminary analysis of the pros and cons of different regulation approaches, and point out some other elements that regulators should consider. I. An overview of the current AI regulation landscape around the world The EU has its lead in legislation, with its parliament adopting its position on the AI ACT in June 2023, heading into trilogue meetings that aim to reach an agreement by the end of this year.[3] China has also announced its draft National AI ACT, scheduled to enter its National People's Congress before the end of 2023.[4] It already has several administration rules in place, such as the 2021 regulation on recommendation algorithms, the 2022 rules for deep synthesis, and the 2023 draft rules on generative AI.[5] Some other countries have been taking a softer approach, preferring voluntary guidelines and testing schemes. The UK published its AI regulation plans in March, seeking views on its sectoral guideline-based pro-innovation regulation approach.[6] To minimize uncertainty for companies, it proposed a set of regulatory principles to ensure that government bodies develop guidelines in a consistent manner.[7] The US National Institute of Standards and Technology (NIST) released the AI Risk Management Framework in January[8], with a non-binding Blueprint for an AI Bill of Rights published in October 2022, providing guidance on the design and use of AI with a set of principles.[9] It is important to take note that some States have drafted regulations on specific subjects, such as New York City’s Final Regulations on Use of AI in Hiring and Promotion came into force in July 2023.[10] Singapore launched the world’s first AI testing framework and toolkit international pilot in May 2022, with the assistance of AWS, DBS Bank, Google, Meta, Microsoft, Singapore Airlines, etc. After a year of testing, it open-sourced the software toolkit in July 2023, to better develop the system.[11] There are also some countries still undecided on their regulation approach. Australia commenced a public consultation on its AI regulatory framework proposal in June[12], seeking views on its draft AI risk management approach.[13] Taiwan’s government announced in July 2023 to propose a draft AI basic law by September 2023, covering topics such as AI-related definition, privacy protections, data governance, risk management, ethical principles, and industrial promotion.[14] However, the plan was recently postponed, indicating a possible shift towards voluntary or mandatory government principles and guidance, before establishing the law.[15] II. Hard law or soft law? The pros and cons of different regulatory approaches One of the key advantages of hard law in AI regulation is its ability to provide binding legal obligations and legal enforcement mechanisms that ensure accountability and compliance.[16] Hard law also provides greater legal certainty, transparency and remedies for consumers and companies, which is especially important for smaller companies that do not have as many resources to influence and comply with fast-changing soft law.[17] However, the legislative process can be time-consuming, slower to update, and less agile.[18] This poses the risk of stifling innovation, as hard law inevitably cannot keep pace with the rapidly evolving AI technology.[19] In contrast, soft law represents a more flexible and adaptive approach to AI regulation. As the potential of AI still remains largely mysterious, government bodies can formulate principles and guidelines tailored to the regulatory needs of different industry sectors.[20] In addition, if there are adequate incentives in place for actors to comply, the cost of enforcement could be much lower than hard laws. Governments can also experiment with several different soft law approaches to test their effectiveness.[21] However, the voluntary nature of soft law and the lack of legal enforcement mechanisms could lead to inconsistent adoption and undermine the effectiveness of these guidelines, potentially leaving critical gaps in addressing AI's risks.[22] Additionally, in cases of AI-related harms, soft law could not offer effective protection on consumer rights and human rights, as there is no clear legal obligation to facilitate accountability and remedies.[23] Carlos Ignacio Gutierrez and Gary Marchant, faculty members at Arizona State University (ASU), analyzed 634 AI soft law programs against 100 criteria and found that two-thirds of the program lack enforcement mechanisms to deliver its anticipated AI governance goals. He pointed out that credible indirect enforcement mechanisms and a perception of legitimacy are two critical elements that could strengthen soft law’s effectiveness.[24] For example, to publish stem cell research in top academic journals, the author needs to demonstrate that the research complies with related research standards.[25] In addition, companies usually have a greater incentive to comply with private standards to avoid regulatory shifts towards hard laws with higher costs and constraints.[26] III. Other considerations Apart from understanding the strengths and limitations of soft law and hard law, it is important for governments to consider each country’s unique differences. For example, Singapore has always focused on voluntary approaches as it acknowledges that being a small country, close cooperation with the industry, research organizations, and other governments to formulate a strong AI governance practice is much more important than rushing into legislation.[27] For them, the flexibility and lower cost of soft regulation provide time to learn from industries to prevent forming rules that aren’t addressing real-world issues.[28] This process allows preparation for better legislation at a later stage. Japan has also shifted towards a softer approach to minimize legal compliance costs, as it recognizes its slower position in the AI race.[29] For them, the EU AI Act is aiming at regulating Giant Tech companies, rather than promoting innovation.[30] That is why Japan considers that hard law does not suit the industry development stage they’re currently in.[31] Therefore, they seek to address legal issues with current laws and draft relevant guidance.[32] IV. Conclusion As the global AI regulatory landscape continues to evolve, it is important for governments to consider the pros and cons of hard law and soft law, and also country-specific conditions in deciding what’s suitable for the country. Additionally, a regular review on the effectiveness and impact of their chosen regulatory approach on AI’s development and the society is recommended. Reference: [1] ChatGPT and Deepfake-Creating Apps: A Running List of Key AI-Lawsuits, TFL, https://www.thefashionlaw.com/from-chatgpt-to-deepfake-creating-apps-a-running-list-of-key-ai-lawsuits/ (last visited Aug 10, 2023); Protection for Voice Actors is Artificial in Today’s Artificial Intelligence World, The National Law Review, https://www.natlawreview.com/article/protection-voice-actors-artificial-today-s-artificial-intelligence-world (last visited Aug 10, 2023). [2] The politics of AI: ChatGPT and political bias, Brookings, https://www.brookings.edu/articles/the-politics-of-ai-chatgpt-and-political-bias/ (last visited Aug 10, 2023); Prospect of AI Producing News Articles Concerns Digital Experts, VOA, https://www.voanews.com/a/prospect-of-ai-producing-news-articles-concerns-digital-experts-/7202519.html (last visited Aug 10, 2023). [3] EU AI Act: first regulation on artificial intelligence, European Parliament, https://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence (last visited Aug 10, 2023). [4] 中國國務院發布立法計畫 年內審議AI法草案,經濟日報(2023/06/09),https://money.udn.com/money/story/5604/7223533 (last visited Aug 10, 2023). [5] id [6] A pro-innovation approach to AI regulation, GOV.UK, https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach/white-paper (last visited Aug 10, 2023). [7] id [8] AI RISK MANAGEMENT FRAMEWORK, NIST, https://www.nist.gov/itl/ai-risk-management-framework (last visited Aug 10, 2023). [9] The White House released an ‘AI Bill of Rights’, CNN, https://edition.cnn.com/2022/10/04/tech/ai-bill-of-rights/index.html (last visited Aug 10, 2023). [10] New York City Adopts Final Regulations on Use of AI in Hiring and Promotion, Extends Enforcement Date to July 5, 2023, Littler https://www.littler.com/publication-press/publication/new-york-city-adopts-final-regulations-use-ai-hiring-and-promotionv (last visited Aug 10, 2023). [11] IMDA, Fact sheet - Open-Sourcing of AI Verify and Set Up of AI Verify Foundation (2023), https://www.imda.gov.sg/-/media/imda/files/news-and-events/media-room/media-releases/2023/06/7-jun---ai-annoucements---annex-a.pdf (last visited Aug 10, 2023). [12] Supporting responsible AI: discussion paper, Australia Government Department of Industry, Science and Resources,https://consult.industry.gov.au/supporting-responsible-ai (last visited Aug 10, 2023). [13] Australian Government Department of Industry, Science and Resources, Safe and responsible AI in Australia (2023), https://storage.googleapis.com/converlens-au-industry/industry/p/prj2452c8e24d7a400c72429/public_assets/Safe-and-responsible-AI-in-Australia-discussion-paper.pdf (last visited Aug 10, 2023). [14] 張璦,中央通訊社,AI基本法草案聚焦隱私保護、應用合法性等7面向 擬設打假中心,https://www.cna.com.tw/news/ait/202307040329.aspx (最後瀏覽日:2023/08/10)。 [15] 蘇思云,中央通訊社,2023/08/01,鄭文燦:考量技術發展快應用廣 AI基本法延後提出,https://www.cna.com.tw/news/afe/202308010228.aspx (最後瀏覽日:2023/08/10)。 [16] supra, note 13, at 27. [17] id. [18] id., at 28. [19] Soft law as a complement to AI regulation, Brookings, https://www.brookings.edu/articles/soft-law-as-a-complement-to-ai-regulation/ (last visited Aug 10, 2023). [20] supra, note 5. [21] Gary Marchant, “Soft Law” Governance of Artificial Intelligence (2019), https://escholarship.org/uc/item/0jq252ks (last visited Aug 10, 2023). [22] How soft law is used in AI governance, Brookings,https://www.brookings.edu/articles/how-soft-law-is-used-in-ai-governance/ (last visited Aug 10, 2023). [23] supra, note 13, at 27. [24] Why Soft Law is the Best Way to Approach the Pacing Problem in AI, Carnegie Council for Ethics in International Affairs,https://www.carnegiecouncil.org/media/article/why-soft-law-is-the-best-way-to-approach-the-pacing-problem-in-ai (last visited Aug 10, 2023). [25] id. [26] id. [27] Singapore is not looking to regulate A.I. just yet, says the city-state’s authority, CNBC,https://www.cnbc.com/2023/06/19/singapore-is-not-looking-to-regulate-ai-just-yet-says-the-city-state.html#:~:text=Singapore%20is%20not%20rushing%20to,Media%20Development%20Authority%2C%20told%20CNBC (last visited Aug 10, 2023). [28] id. [29] Japan leaning toward softer AI rules than EU, official close to deliberations says, Reuters, https://www.reuters.com/technology/japan-leaning-toward-softer-ai-rules-than-eu-source-2023-07-03/ (last visited Aug 10, 2023). [30] id. [31] id. [32] id.