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Vortragende Person/Vortragende Personen:
Diverse

This tutorial, supported by NFDI4DataScience, addresses the replicability crisis in Artificial Intelligence, with a particular focus on machine-based learning approaches. It covers the research data life cycle, emphasizing best practices for data/software management, metadata, documentation, versioning, and sharing practical examples on how to achieve such practices. Additionally, it introduces model and dataset cards for comprehensive reporting, and advocates for the adoption of FAIR Data Principles to transform research outputs into FAIR Data Objects (FDOs). Aimed at academics across all domains working on AI fields, the tutorial provides practical guidance to enhance transparency and accountability, fostering a more reliable and impactful AI landscape.

Program:

•  Hands-On Session 1 - Model and dataset cards. (Angelie Kraft) Participants will learn about documentation schemas that facilitate comprehensive reporting of key information such as model architectures, hyperparameters, and dataset characteristics. By standardizing documentation practices, researchers can enhance the reproducibility of experiments, foster collaboration across diverse AI domains, and foster transparency regarding biases and limitations.
• Hands-On Session 2 - Adopting the FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable). (Leyla Jael Castro) Participants will learn how to align their AI research outputs to the FAIR principles and transform them into Software Management Plans, enabling seamless integration with existing data infrastructures and maximizing the impact of their work. 
• Hands-On Session 3 - RO-Crate + Sign Posting to create FDOs. (Leyla Jael Castro or Team) 

Further information and registration