Artificial Intelligence (AI) is rapidly transforming our world, offering tremendous potential to improve efficiency, productivity, and decision-making across various industries. However, AI systems’ increasing complexity and pervasiveness raise concerns about their ethical implications and the need for transparency. This tutorial will provide a comprehensive overview of the principles and practices for fostering integrity in AI development and deployment. The tutorial will consist of five presentations, a practical hands-on session, and a panel discussion with experts from industry and academia.
Objectives
Learn to apply ethical principles to data-driven decision-making and develop integrity indicators to measure and monitor data and algorithm quality
Gain insights into the latest trends in explainable AI (XAI) and how to implement XAI techniques
Discover practical applications of XAI in various industries, including finance, healthcare, and law, and network with experts in the fields of data ethics, integrity, and XAI to share best practices and stay up-to-date on the latest developments
Enhance the understanding of the ethical implications of AI and develop strategies for building trustworthy and responsible AI systems
Agenda
13:30 – 14:00
Intro
Tutorial Intro
Digital Ethics Umbrella
14:00 – 15:00
Theory
Ethical and Explainable Recommender Systems Intro
XAI in Financial Sector
XAI in Healthcare
XAI in Large Language Models
15:00 – 15:30
Break
15:30 – 16:15
Practice
Data and AI Integrity Framework
Practical Session: Measuring Integrity
16:15 – 17:00
Interactive panel
Participation of business and academic experts
Organizers
Prof. Dr. Branka Hadji Misheva, Bern University of Applied Sciences
Prof. Dr. Bruno Frischherz, Lucerne University of Applied Sciences and Arts
Dr. Guang Lu, Lucerne University of Applied Sciences and Arts
PD Dr. Luis Terán, Lucerne University of Applied Sciences and Arts
Dr. Omran Ayoub, University of Applied Sciences and Arts of Southern Switzerland
Dr. Sarah Seyr, Lucerne University of Applied Sciences and Arts