The Key Elements for Data Intermediaries to Deliver Their Promise

The Key Elements for Data Intermediaries to Deliver Their Promise

2022/12/13

  As human history enters the era of data economy, data has become the new oil. It feeds artificial intelligence algorithms that are disrupting how advertising, healthcare, transportation, insurance, and many other industries work. The excitement of having data as a key production input lies in the fact that it is a non-rivalrous good that does not diminish by consumption.[1] However, the fact that people are reluctant in sharing data due to privacy and trade secrets considerations has been preventing countries to realize the full value of data. [2]

  To release more data, policymakers and researchers have been exploring ways to overcome the trust dilemma. Of all the discussions, data intermediaries have become a major solution that governments are turning to. This article gives an overview of relevant policy developments concerning data intermediaries and a preliminary analysis of the key elements that policymakers should consider for data intermediaries to function well.

I. Policy and Legal developments concerning data intermediaries

  In order to unlock data’s full value, many countries have started to focus on data intermediaries. For example, in 2021, the UK’s Department for Digital, Culture, Media and Sport (DCMS) commissioned the Centre for Data Ethics and Innovation (CDEI) to publish a report on data intermediaries[3] , in response to the 2020 National Data Strategy.[4] In 2020, the European Commission published its draft Data Governance Act (DGA)[5] , which aims to build up trust in data intermediaries and data altruism organizations, in response to the 2020 European Strategy for Data.[6] The act was adopted and approved in mid-2022 by the Parliament and Council; and will apply from 24 September 2023.[7] The Japanese government has also promoted the establishment of data intermediaries since 2019, publishing guidance to establish regulations on data trust and data banks.[8]

II. Key considerations for designing effective data intermediary policy

1.Evaluate which type of data intermediary works best in the targeted country

  From CDEI’s report on data intermediaries and the confusion in DGA’s various versions of data intermediary’s definition, one could tell that there are many forms of data intermediaries. In fact, there are at least eight types of data intermediaries, including personal information management systems (PIMS), data custodians, data exchanges, industrial data platforms, data collaboratives, trusted third parties, data cooperatives, and data trusts.[9] Each type of data intermediary was designed to combat data-sharing issues in specific countries, cultures, and scenarios. Hence, policymakers need to evaluate which type of data intermediary is more suitable for their society and market culture, before investing more resources to promote them.

  For example, data trust came from the concept of trust—a trustee managing a trustor’s property rights on behalf of his interest. This practice emerged in the middle ages in England and has since developed into case law.[10] Thus, the idea of data trust is easily understood and trusted by the British people and companies. As a result, British people are more willing to believe that data trusts will manage their data on their behalf in their best interest and share their valuable data, compared to countries without a strong legal history of trusts. With more people sharing their data, trusts would have more bargaining power to negotiate contract terms that are more beneficial to data subjects than what individual data owners could have achieved. However, this model would not necessarily work for other countries without a strong foundation of trust law.

2.Quality signals required to build trust: A government certificate system can help overcome the lemon market problem

  The basis of trust in data intermediaries depends largely on whether the service provider is really neutral in its actions and does not reuse or sell off other parties’ data in secret. However, without a suitable way to signal their service quality, the market would end up with less high-quality service, as consumers would be reluctant to pay for higher-priced service that is more secure and trustworthy when they have no means to verify the exact quality.[11] This lemon market problem could only be solved by a certificate system established by actors that consumers trust, which in most cases is the government.

  The EU government clearly grasped this issue as a major obstacle to the encouragement of trust in data intermediaries and thus tackles it with a government register and verification system. According to the Data Government Act, data intermediation services providers who intend to provide services are required to notify the competent authority with information on their legal status, form, ownership structure, relevant subsidiaries, address, public website, contact details, the type of service they intend to provide, the estimated start date of activities…etc. This information would be provided on a website for consumers to review. In addition, they can request the competent authority to confirm their legal compliance status, which would in turn verify them as reliable entities that can use the ‘data intermediation services provider recognised in the Union’ label.

3.Overcoming trust issues with technology that self-enforces privacy: privacy-enhancing technologies (PETs)

  Even if there are verified data intermediation services available, businesses and consumers might still be reluctant to trust human organizations. A way to boost trust is to adopt technologies that self-enforces privacy. A real-world example is OpenSAFELY, a data intermediary implementing privacy-enhancing technologies (PETs) to provide health data sharing in a secure environment. Through a federated analytics system, researchers are able to conduct research with large volumes of healthcare data, without the ability to observe any data directly. Under such protection, UK NHS is willing to share its data for research purposes. The accuracy and timeliness of such research have provided key insights to inform the UK government in decision-making during the COVID-19 pandemic.

  With the benefits it can bring, unsurprisingly, PETs-related policies have become quite popular around the globe. In June 2022, Singapore launched its Digital Trust Centre (DTC) for accelerating PETs development and also signed a Memorandum of Understanding with the International Centre of Expertise of Montreal for the Advancement of Artificial Intelligence (CEIMIA) to collaborate on PETs.[12] On September 7th, 2022, the UK Information Commissioners’ Office (ICO) published draft guidance on PETs.[13] Moreover, the U.K. and U.S. governments are collaborating on PETs prize challenges, announcing the first phase winners on November 10th, 2022.[14] We could reasonably predict that more PETs-related policies would emerge in the coming year.

[1] Yan Carrière-Swallow and Vikram Haksar, The Economics of Data, IMFBlog (Sept. 23, 2019), https://blogs.imf.org/2019/09/23/the-economics-of-data/#:~:text=Data%20has%20become%20a%20key,including%20oil%2C%20in%20important%20ways (last visited July 22, 2022).

[2] Frontier Economics, Increasing access to data across the economy: Report prepared for the Department for Digital, Culture, Media, and Sport (2021), https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/974532/Frontier-access_to_data_report-26-03-2021.pdf (last visited July 22, 2022).

[3] The Centre for Data Ethics and Innovation (CDEI), Unlocking the value of data: Exploring the role of data intermediaries (2021), https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1004925/Data_intermediaries_-_accessible_version.pdf (last visited June 17, 2022).

[4] Please refer to the guidelines for the selection of sponsors of the 2022 Social Innovation Summit: https://www.gov.uk/government/publications/uk-national-data-strategy/national-data-strategy (last visited June 17, 2022).

[5] Regulation of the European Parliament and of the Council on European data governance and amending Regulation (EU) 2018/1724 (Data Governance Act), 2020/0340 (COD) final (May 4, 2022).

[6] Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and The Committee of the Regions— A European strategy for data, COM/2020/66 final (Feb 19, 2020).

[7] Proposal for a Regulation on European Data Governance, European Parliament Legislative Train Schedule, https://www.europarl.europa.eu/legislative-train/theme-a-europe-fit-for-the-digital-age/file-data-governance-act(last visited Aug 17, 2022).

[8] 周晨蕙,〈日本資訊信託功能認定指引第二版〉,科技法律研究所,https://stli.iii.org.tw/article-detail.aspx?no=67&tp=5&d=8422(最後瀏覽日期︰2022/05/30)。

[9] CDEI, supra note 3.

[10] Ada Lovelace Institute, Exploring legal mechanisms for data stewardship (2021), 30~31,https://www.adalovelaceinstitute.org/wp-content/uploads/2021/03/Legal-mechanisms-for-data-stewardship_report_Ada_AI-Council-2.pdf (last visited Aug 17, 2022).

[11] George A. Akerlof, The Market for "Lemons": Quality Uncertainty and the Market Mechanism, THE QUARTERLY JOURNAL OF ECONOMICS, 84(3), 488-500 (1970).

[12] IMDA, MOU Signing Between IMDA and CEIMIA is a Step Forward in Cross-border Collaboration on Privacy Enhancing Technology (PET) (2022),https://www.imda.gov.sg/-/media/Imda/Files/News-and-Events/Media-Room/Media-Releases/2022/06/MOU-bet-IMDA-and-CEIMIA---ATxSG-1-Jun-2022.pdf (last visited Nov. 28, 2022).

[13] ICO publishes guidance on privacy enhancing technologies, ICO, https://ico.org.uk/about-the-ico/media-centre/news-and-blogs/2022/09/ico-publishes-guidance-on-privacy-enhancing-technologies/ (last visited Nov. 27, 2022).

[14] U.K. and U.S. governments collaborate on prize challenges to accelerate development and adoption of privacy-enhancing technologies, GOV.UK, https://www.gov.uk/government/news/uk-and-us-governments-collaborate-on-prize-challenges-to-accelerate-development-and-adoption-of-privacy-enhancing-technologies (last visited Nov. 28, 2022); Winners Announced in First Phase of UK-US Privacy-Enhancing Technologies Prize Challenges, NIST, https://www.nist.gov/news-events/news/2022/11/winners-announced-first-phase-uk-us-privacy-enhancing-technologies-prize (last visited Nov. 28, 2022).

※The Key Elements for Data Intermediaries to Deliver Their Promise,STLI, https://stli.iii.org.tw/en/article-detail.aspx?no=55&tp=2&i=168&d=8923 (Date:2025/03/28)
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The opening and sharing of scientific data- The Data Policy of the U.S. National Institutes of Health

The opening and sharing of scientific data- The Data Policy of the U.S. National Institutes of Health Li-Ting Tsai   Scientific research improves the well-being of all mankind, the data sharing on medical and health promote the overall amount of energy in research field. For promoting the access of scientific data and research findings which was supported by the government, the U.S. government affirmed in principle that the development of science was related to the retention and accesses of data. The disclosure of information should comply with legal restrictions, and the limitation by time as well. For government-sponsored research, the data produced was based on the principle of free access, and government policies should also consider the actual situation of international cooperation[1]Furthermore, the access of scientific research data would help to promote scientific development, therefore while formulating a sharing policy, the government should also consider the situation of international cooperation, and discuss the strategy of data disclosure based on the principle of free access.   In order to increase the effectiveness of scientific data, the U.S. National Institutes of Health (NIH) set up the Office of Science Policy (OSP) to formulate a policy which included a wide range of issues, such as biosafety (biosecurity), genetic testing, genomic data sharing, human subjects protections, the organization and management of the NIH, and the outputs and value of NIH-funded research. Through extensive analysis and reports, proposed emerging policy recommendations.[2] At the level of scientific data sharing, NIH focused on "genes and health" and "scientific data management". The progress of biomedical research depended on the access of scientific data; sharing scientific data was helpful to verify research results. Researchers integrated data to strengthen analysis, promoted the reuse of difficult-generated data, and accelerated research progress.[3] NIH promoted the use of scientific data through data management to verify and share research results.   For assisting data sharing, NIH had issued a data management and sharing policy (DMS Policy), which aimed to promote the sharing of scientific data funded or conducted by NIH.[4] DMS Policy defines “scientific data.” as “The recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings, regardless of whether the data are used to support scholarly publications. Scientific data do not include laboratory notebooks, preliminary analyses, completed case report forms, drafts of scientific papers, plans for future research, peer reviews, communications with colleagues, or physical objects, such as laboratory specimens.”[5] In other words, for determining scientific data, it is not only based on whether the data can support academic publications, but also based on whether the scientific data is a record of facts and whether the research results can be repeatedly verified.   In addition, NIH, NIH research institutes, centers, and offices have had expected sharing of data, such as: scientific data sharing, related standards, database selection, time limitation, applicable and presented in the plan; if not applicable, the researcher should propose the data sharing and management methods in the plan. NIH also recommended that the management and sharing of data should implement the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The types of data to be shared should first in general descriptions and estimates, the second was to list meta-data and other documents that would help to explain scientific data. NIH encouraged the sharing of scientific data as soon as possible, no later than the publication or implementation period.[6] It was said that even each research project was not suitable for the existing sharing strategy, when planning a proposal, the research team should still develop a suitable method for sharing and management, and follow the FAIR principles.   The scientific research data which was provided by the research team would be stored in a database which was designated by the policy or funder. NIH proposed a list of recommended databases lists[7], and described the characteristics of ideal storage databases as “have unique and persistent identifiers, a long-term and sustainable data management plan, set up metadata, organizing data and quality assurance, free and easy access, broad and measured reuse, clear use guidance, security and integrity, confidentiality, common format, provenance and data retention policy”[8]. That is to say, the design of the database should be easy to search scientific data, and should maintain the security, integrity and confidentiality and so on of the data while accessing them.   In the practical application of NIH shared data, in order to share genetic research data, NIH proposed a Genomic Data Sharing (GDS) Policy in 2014, including NIH funding guidelines and contracts; NIH’s GDS policy applied to all NIHs Funded research, the generated large-scale human or non-human genetic data would be used in subsequent research. [9] This can effectively promote genetic research forward.   The GDS policy obliged researchers to provide genomic data; researchers who access genomic data should also abide by the terms that they used the Controlled-Access Data for research.[10] After NIH approved, researchers could use the NIH Controlled-Access Data for secondary research.[11] Reviewed by NIH Data Access Committee, while researchers accessed data must follow the terms which was using Controlled-Access Data for research reason.[12] The Genomic Summary Results (GSR) was belong to NIH policy,[13] and according to the purpose of GDS policy, GSR was defined as summary statistics which was provided by researchers, and non-sensitive data was included to the database that was designated by NIH.[14] Namely. NIH used the application and approval of control access data to strike a balance between the data of limitation access and scientific development.   For responding the COVID-19 and accelerating the development of treatments and vaccines, NIH's data sharing and management policy alleviated the global scientific community’s need for opening and sharing scientific data. This policy established data sharing as a basic component in the research process.[15] In conclusion, internalizing data sharing in the research process will help to update the research process globally and face the scientific challenges of all mankind together. [1]NATIONAL SCIENCE AND TECHNOLOGY COUNCIL, COMMITTEE ON SCIENCE, SUBCOMMITEE ON INTERNATIONAL ISSUES, INTERAGENCY WORKING GROUP ON OPEN DATA SHARING POLICY, Principles For Promoting Access To Federal Government-Supported Scientific Data And Research Findings Through International Scientific Cooperation (2016), 1, organized from Principles, at 5-8, https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/NSTC/iwgodsp_principles_0.pdf (last visited December 14, 2020). [2]About Us, Welcome to NIH Office of Science Policy, NIH National Institutes of Health Office of Science Policy, https://osp.od.nih.gov/about-us/ (last visited December 7, 2020). [3]NIH Data Management and Sharing Activities Related to Public Access and Open Science, NIH National Institutes of Health Office of Science Policy, https://osp.od.nih.gov/scientific-sharing/nih-data-management-and-sharing-activities-related-to-public-access-and-open-science/ (last visited December 10, 2020). [4]Final NIH Policy for Data Management and Sharing, NIH National Institutes of Health Office of Extramural Research, Office of The Director, National Institutes of Health (OD), https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-013.html (last visited December 11, 2020). [5]Final NIH Policy for Data Management and Sharing, NIH National Institutes of Health Office of Extramural Research, Office of The Director, National Institutes of Health (OD), https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-013.html (last visited December 12, 2020). [6]Supplemental Information to the NIH Policy for Data Management and Sharing: Elements of an NIH Data Management and Sharing Plan, Office of The Director, National Institutes of Health (OD), https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-014.html (last visited December 13, 2020). [7]The list of databases in details please see:Open Domain-Specific Data Sharing Repositories, NIH National Library of Medicine, https://www.nlm.nih.gov/NIHbmic/domain_specific_repositories.html (last visited December 24, 2020). [8]Supplemental Information to the NIH Policy for Data Management and Sharing: Selecting a Repository for Data Resulting from NIH-Supported Research, Office of The Director, National Institutes of Health (OD), https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-016.html (last visited December 13, 2020). [9]NIH Genomic Data Sharing, National Institutes of Health Office of Science Policy, https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/ (last visited December 15, 2020). [10]NIH Genomic Data Sharing Policy, National Institutes of Health (NIH), https://grants.nih.gov/grants/guide/notice-files/NOT-OD-14-124.html (last visited December 17, 2020). [11]NIH Genomic Data Sharing Policy, National Institutes of Health (NIH), https://grants.nih.gov/grants/guide/notice-files/NOT-OD-14-124.html (last visited December 17, 2020). [12]id. [13]NIH National Institutes of Health Turning Discovery into Health, Responsible Use of Human Genomic Data An Informational Resource, 1, at 6, https://osp.od.nih.gov/wp-content/uploads/Responsible_Use_of_Human_Genomic_Data_Informational_Resource.pdf (last visited December 17, 2020). [14]Update to NIH Management of Genomic Summary Results Access, National Institutes of Health (NIH), https://grants.nih.gov/grants/guide/notice-files/NOT-OD-19-023.html (last visited December 17, 2020). [15]Francis S. Collins, Statement on Final NIH Policy for Data Management and Sharing, National Institutes of Health Turning Discovery Into Health, https://www.nih.gov/about-nih/who-we-are/nih-director/statements/statement-final-nih-policy-data-management-sharing (last visited December 14, 2020).

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