Algorithms are the beating heart of artificial intelligence. They determine how data is analyzed, predictions are made and decisions are made. Whether in fraud detection, risk classification or automated decision-making in the public sector - the operation of algorithms and the models based on them raises fundamental questions about transparency, explainability and direction.

In this topic file "Algorithms and Models" we will discuss:

• What exactly algorithms and models are, and how they relate to each other in AI applications.
• The differences between traditional control systems, machine learning and deep learning.
• Challenges of explainability (explainability), bias, discrimination and model selection.
• Relevant laws and regulations, including the AI Act, the AVG (for automated decision-making), and national frameworks such as the Algorithm Register and the Human Rights and Algorithms Impact Assessment (IAMA).
• Case studies of algorithm use within government, healthcare and financial services, including lessons learned and pitfalls.