This is slightly old news, but the OECD published an interesting document on February 5. 1
- Enhancing Access to and Sharing of Data in the Age of Artificial Intelligence
—Companion Document to the OECD Council Recommendation on Enhancing Access to and
Sharing of Data - Enhancing Access to and Sharing of Data in the Age of AI
—Companion Document to the OECD Council Recommendation on Enhancing Access to and Sharing of Data
The following is a broad summary of its contents. Please consult the original document for details.
Overview
- The OECD Recommendation on Enhancing Access to and Sharing of Data (EASD) provides a framework for maximizing the benefits of data while ensuring that rights are protected.
- It emphasizes a whole-of-government approach to data governance that integrates economic, social, and legal considerations.
- The Recommendation encourages voluntary adherence by OECD members and partners and promotes responsible data-sharing practices.
Key Concepts in Data Governance
- Data value cycle: This covers the entire data life cycle, from creation to deletion, and highlights the need for complementary resources such as algorithms and human skills.
- Data openness continuum: A framework that classifies access from closed to open data, enabling tailored sharing arrangements based on risk and trust.
- Data ecosystems: Various stakeholders, including data holders, producers, and intermediaries, interact to create value, requiring cooperation and trust to balance competing interests.
Principles for Enhancing Data Access and Sharing
- Strengthening trust: Engage stakeholders through consultation and transparency to build trust in data governance.
- Investing in data: Promote market-based approaches and sustainable business models to facilitate data sharing, including regulatory sandboxes for innovation.
- Effective use of data: Ensure that data is findable, accessible, interoperable, and reusable (the FAIR principles), and facilitate cross-border data sharing.
Practical Applications and Impacts
- Governments should adopt national data strategies aligned with the EASD principles to promote responsible data governance.
- Coherent legal frameworks are needed to support data sharing while protecting privacy and intellectual property rights.
- Successful examples from various countries demonstrate the importance of public-private partnerships and stakeholder engagement in enhancing data access.
Strengthening Data-Sharing Infrastructure
- Centralized data repositories: Establish centralized infrastructure to facilitate efficient information sharing among public institutions, improve service delivery, and support data-driven public policy.
- Public engagement: Promote responsible data-sharing practices to improve public understanding of the benefits and risks of a data-driven economy.
- Stakeholder input: Involve stakeholders in regulatory discussions to address AI-related risks without hindering innovation.
Implications for Competition Authorities
- Market dynamics: Consider the effects of multisided business models that offer free products in exchange for consumer data and may entrench market power.
- Demand-side characteristics: Recognize that data can affect market dynamics, search costs, switching costs, and consumer choice, potentially reinforcing dominant market positions.
Responsible Data Sharing in Research
- Research data sharing: Follow guidelines aligned with the Australian Code for the Responsible Conduct of Research to promote data sharing among institutions and researchers.
- License classification: Develop a classification of data licenses to clarify responsibilities and rights associated with the use of data in AI and machine learning.
Open Government Data and AI
- Importance of open data: Open government data is essential for developing and training AI systems and serves as a trustworthy input.
- Risk management: Open data helps manage risks related to data provenance and source reliability, enhancing the integrity of AI applications.
Concluding Insights
- The OECD Recommendation on EASD provides a comprehensive framework for enhancing data access and sharing, emphasizing trust, investment, and effective governance.
- Implementing these principles could improve public services, foster innovation in the data-driven economy, and advance the development of responsible AI.
- Continued stakeholder engagement and adherence to established guidelines are essential to realizing the full potential of data-sharing initiatives.
Policy Brief:
A related Policy Brief was also published in 3/8.
Footnotes
- On February 6, it also published a Policy Brief on this subject.
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