💬 Course feedback
🎉 Congratulations on completing the AI‑ready data and FAIR data management for industry course!
Your feedback helps us improve the course content, learning experience, and future training activities. The survey takes only a few minutes to complete, and all responses are valuable.
We appreciate your time and participation. Thank you for learning with us!
📖 Further reading
- AI Policy Observatory: Policies, data, and analysis for trustworthy artificial intelligence.
- OECD AI Principles: The foundational global framework for trustworthy AI, emphasizing transparency, accountability, data governance, and human oversight.
- Catalogue of Tools & Metrics for Trustworthy AI
- Microsoft Responsible AI Transparency Report (2025)
- Pei-Hung Lin, Chunhua Liao, Winson Chen, Tristan Vanderbruggen, Murali Emani, Hailu Xu. Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study.
- Responsible AI in Practice - UNESCO & Thomson Reuters Foundation (2026): Practical insights into how companies implement responsible AI, with a focus on data governance, training data controls, and transparency gaps.
- Big Data Value Association
- What is AI-Ready Data?
- What It Means To Be 'AI-Ready' - Forbes
- Experts Share Practices to Overcome AI Data Readiness