Rationale and Objectives
Artificial Intelligence (AI) has rapidly transformed various sectors, further driven by mainstream adoption of generative AI technologies like ChatGPT. This swift progress has sparked both high expectations and uncertainties about AI’s impact. AI relies on data, software, and computational resources to generate outputs like predictions and decisions. Significant investments are being made globally, both by public and private entities. However, challenges like data quality, legal issues, and the digital divide must be addressed to harness AI responsibly. Recognizing these trends, the Research Data Alliance (RDA) has taken the first step by generating a report, based on two pilot workshops held in September 2024 for Organisational Assembly and Funders Forum members, repeated twice to accommodate geographical diversity and timezones. These workshops fostered discussions on AI, exploring opportunities and challenges for the RDA community and beyond.
Analysis and Conclusion of Key Focus Areas: Next Steps for the RDA Secertariat and global RDA Community
Next steps for the RDA Secretariat
When asked about their topics of interest for future activities, participants suggested several ideas for the RDA Secretariat to organise and coordinate. In total 10 areas were identified and the funders and organisational members prioritised the following 5 areas:
- Addressing inequalities in Open Science
- Establish Best Practices for Data Readiness and Responsible AI
- Discussing the Latest Trends in AI
- Researchers are leveraging AI across disciplines
- Showcasing RDA group outputs related to AI
At the time of circulating this report (November 2024), the RDA Secretariat plans with respect to the priority areas are:
Organise a short series of webinars/talks on AI to address these areas with a focus on what researchers are doing with AI.
Next steps for the global RDA Community
Building on the session discussions and written feedback, and leveraging its expertise and strengths, the RDA has identified the following key actionable areas where it can provide the most value to AI:
- Sensitive Data Management and Trust Building: To address the challenges of using sensitive data in AI applications, it’s essential to promote technical solutions like privacy-enhancing technologies while also overcoming social barriers such as trust and awareness among stakeholders.
- Data Readiness for AI—Quality, FAIRness, and Fairness: Enhancing data quality and integrity beyond FAIR principles to ensure datasets are truly AI-ready. This involves focusing on both technical standards and ethical guidelines to promote FAIR data and data fairness in AI systems.
- Sustainability of Technical and Human Infrastructures for AI: Ensuring long-term sustainability by developing scalable technical platforms and investing in skill development and career pathways for professionals who maintain and innovate within AI infrastructures.
- Bridging the Policy-Technical Implementation Gap in AI: Facilitating collaboration between policymakers, technologists, and end-users to translate ethical guidelines and regulations into practical tools and systems.