Explainable AI - Training Week

The training week provides a comprehensive introduction to Explainable Artificial Intelligence (XAI), emphasizing both foundational and advanced methodologies. Participants will explore practical applications of model-agnostic techniques such as Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) plots, LIME, and SHAP, alongside specialized approaches for interpreting complex deep learning models. Particular attention will be given to explainability in neural networks, including gradient-based methods, as well as emerging XAI techniques tailored to time-series data. Participants will explore how these techniques enhance interpretability and transparency in AI systems, along with the challenges they face, such as scalability, interpretability trade-offs, and accuracy limitations.

The course also investigates the limitations and reliability of XAI models when applied to complex datasets, with advanced discussions on their performance and practical constraints. A distinctive focus is placed on financial applications, examining how XAI can address the unique challenges and regulatory requirements of the financial sector. 

By the end of the course, participants will gain the expertise to implement XAI models, critically evaluate their effectiveness, and apply them responsibly within financial systems, fostering trust and compliance with regulatory standards.

Modules and Credits:  The training week covers the following models and credits of the MSCA doctoral program:

• 6-9th October, 2025: Foundation Module "Need for Explainable AI in Finance" (4 ECT)

• 10th October, 2025: Module "Research Ethics and Sustainable Research Management" (1 ECT)

Prior Knowledge:  Participants are expected to have a foundational understanding of machine learning concepts, including supervised and unsupervised learning, common algorithms, and evaluation metrics. Certain familiarity with Python programming is needed, with experience using libraries such as scikit-learn, pandas, and NumPy. Familiarity with basic statistics and linear algebra will also be helpful for understanding the mathematical foundations of explainability methods. 

Detailed Schedule

Speakers and Talks

Milos Maricic (CEO, Altruist League): Explainable AI for Philanthropy and Finance

Milos Maricic is a philanthropy expert, author, and former humanitarian executive pioneering the use of artificial intelligence in global giving. He is the founder of the Altruist League, a Geneva-based consultancy that has reshaped how foundations and investors approach systemic challenges through AI-driven funding strategies.

Talk: Explainable AI for Philanthropy and Finance

In this session, Milos Maricic, CEO of the Altruist League, explores how Explainable AI (XAI) is transforming high-stakes decision-making in philanthropy and finance. Drawing on real-world examples from his organization’s work with global foundations and institutional investors, he will show how tools like SHAP and LIME can bring much-needed transparency to AI-driven grant allocation and risk analysis. The talk will address the unique ethical, regulatory, and strategic challenges of applying XAI in contexts where social outcomes, not just profit, are at stake. With a focus on interpretability as a cornerstone of trust, Milos will share lessons on navigating the trade-offs between explainability, accuracy, and impact in mission-driven AI systems.

Accommodation Options

To help with planning your stay, we have compiled a list of affordable accommodation options in Bern, which are near the university campus.