Welcome and Introduction

• Panče Panov

Room: predavalnica Elektronike na Teslovi 30

Block 1: AI data lifecycle & FAIR principles

• Panče Panov

Room: predavalnica Elektronike na Teslovi 30 Introduction to AI data assets (raw data, processed data, labels, dataset splits, features, evaluation artefacts) and how they evolve across the data lifecycle. Review of the FAIR principles and what each letter means in the context of AI projects.

Block 2: Finding, sharing & interoperability

• Panče Panov

Room: predavalnica Elektronike na Teslovi 30 Practical session on how to find, access and reuse AI datasets, including access conditions and licensing. Discussion of interoperability for AI: file formats, metadata schemas, label definitions and basic standards. Overview of data repositories and sharing strategies for AI datasets and related artefacts.

Block 3: Sensitive data, ethics, industry constraints & DMP

• Panče Panov

Room: predavalnica Elektronike na Teslovi 30 Working with confidential, personal and sensitive data in AI, and the main ethical considerations. Open data and open science versus industrial constraints (governance, IP, security). Structure of an AI-focused data management plan and supporting tools.

Wrap-up and Q&A

• Panče Panov

Room: predavalnica Elektronike na Teslovi 30