As AI adoption accelerates across industries, ensuring ethical integrity and reproducibility has become increasingly critical for enterprises and developers. This tutorial presents a Retrieval-Augmented Generation (RAG)-based compliance plug-in designed to promote responsible AI practices. Through a hands-on session, participants will learn how to integrate external compliance knowledge bases with generative models to automate ethical checks, document decision-making processes, and enhance the reproducibility of AI outputs. The session will cover system architecture, implementation using popular frameworks, and practical use cases, equipping attendees with tools to embed trust and accountability into AI workflows from the outset.
Over the course of 90 minutes, we will introduce the core concepts behind the Python-based plug-in, including RAG architecture and vector-based retrieval techniques. Participants will engage with live demonstrations on querying regulatory standards such as the European Union Artificial Intelligence Act and FAIR (Findable, Accessible, Interoperable, Reusable) principles. The tutorial will also showcase bias auditing and model transparency features, using a healthcare case study to illustrate real-world application and highlight model tracking and reproducibility capabilities.

This talk presents a python-Streamlit application which has been developed based on integration of deep learning based automatic chess move detection and LLM-generated chess game commentary and is designed to be a powerful tool for enhancing chess learning and viewer engagement. Automatic move detection based on a high accuracy computer vision model allows chess players, learners and general viewers to accurately track the games, identify mistakes, and review tactics without the need for manual notation. Beginners gain a clearer understanding of gameplay flow, while enthusiasts can easily annotate and revisit key moments. By combining move detection with real-time, LLM-driven commentary, the system provides context-aware explanations that highlight strategic ideas, tactical patterns, and player intentions. This creates an interactive and educational experience that enriches both learning and viewing.