Data Rules for Machine Learning: How Europe can Unlock the Potential While Mitigating the Risks
Innovation Board in the proposed Data Governance Act, given its intended focus on data standardization.77 The authority’s functions could include: ■ Serving as a single window for data-access requests for data sets across multiple jurisdictions and coordinate access to various data sets under single or at least compatible format and license ■ Assessing requests for data not available for open access, including sensitive data, in consultation with national data-protection authorities and make recommendations to relevant public bodies on whether to allow access and under what conditions, such as the use of specific privacy-preserving techniques and security precautions The service could operate on a cost-recovery basis, passing expenses on to the businesses and research institutes using the service, which would still help reduce costs arising from administrative and legal complexity for businesses seeking to reuse public-sector data. It would also increase the availability of sensitive data for research and development while ensuring adequate protections laid out in relevant national-level data-protection legislation, for example, through privacy-preserving machine learning techniques.
1.2 Harmonize data standards for interoperability Machine learning and the Internet of Things (IoT) will bring artificial intelligence to our devices, factories, hospitals, and homes, creating efficiencies and driving productivity.78 To achieve this, however, the EU needs to enable the sharing of high-quality data between distributed devices and systems.79 The lack of harmonized data standards across
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#DataRules
the EU80 slows the deployment of machine learning compared to China and the United States.81 To address this, the European Commission should facilitate the development of sector-specific data models, application programming interfaces (APIs) and open source software that enable the seamless exchange and translation of differently structured data across systems and organizations.
EU Policy Context82 The European Commission’s efforts to encourage the voluntary adoption of data interoperability standards have made slow progress. For example, decadelong efforts by the commission under the European Patient Smart Open Services (epSOS) project to encourage the implementation of interoperable health data standards have been stymied by reluctant healthcare IT providers and ambivalent national governments.83 More recently, the commission enacted the 2019 Recommendation on a Europe Electronic Health Record exchange, but it lacks legal enforcement to incentivize compliance.84 Looking forward, the EU’s 2020 Data Strategy included the development of rules for common “data spaces” intended to “set up structures that enable organizations to share data.” The European Commission’s 2020 proposal for a Data Governance Act also includes the establishment of a European Data Innovation Board that would focus on data standardization.85 However, it will be very difficult for member states to agree to a standard data format due to constraints by existing IT infrastructure as well as incompatible and inflexible national privacy and security regulations.86 To facilitate widespread adoption of machine learning, the commission needs to find a way to enable data interoperability while avoiding
European Commission, Proposal for a Regulation on European Data Governance (Data Governance Act). “Generating Value at Scale with Industrial IoT,” McKinsey Digital (website), McKinsey & Company, February 5, 2021, https://www.mckinsey.com/businessfunctions/mckinsey-digital/our-insights/a-manufacturers-guide-to-generating-value-at-scale-with-industrial-iot. Gudivada, Apon, and Ding, “Data Quality Considerations for Big Data.” Daniel Rubinfeld, “Data Portability,” Competition Policy International, November 26, 2020, https://www.competitionpolicyinternational.com/dataportability/. Winston Maxwell et al., A Comparison of IoT Regulatory Uncertainty in the EU, China, and the United States, Hogan Lovells (law firm including Hogan Lovells International LLP, Hogan Lovells US LLP, and affiliated businesses), March 2019. This chapter focuses on electronic health records (EHR) as a case study on data interoperability. The siloed nature of healthcare IT systems, combined with incompatible national privacy and security rules, provide a good demonstration of the challenges the EU will face more broadly in connecting distributed data systems to leverage the benefits of machine learning. “EpSOS,” healthcare-in-europe.com (platform website), accessed 2021, https://healthcare-in-europe.com/en/news/epsos.html. European Commission, Recommendation on a European Electronic Health Record Exchange Format, C(2019)800 of February 6, 2019, European Commission (website), accessed 2021, https://digital-strategy.ec.europa.eu/en/library/recommendation-european-electronic-health-record-exchangeformat. European Commission, Proposal for a Regulation on European Data Governance (Data Governance Act). Philipp Grätzel von Grätz, “Transforming Healthcare Systems the European Way,” Healthcare IT News, May 24, 2019, https://www.healthcareitnews.com/ news/emea/transforming-healthcare-systems-european-way.
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