Name: MSCA Training Week - UT
Start: 2025-02-03T08:30:00.000Z
End: 2025-02-07T16:30:00.000Z
Location: Ravelijn building, University of Twente

Title of talk: **Ethical Requirements for AI Systems**

Ethical behavior is an integral part of people’s everyday life whether they are aware of this or not. When a teacher grades exams of dozens of students aiming at consistency and transparency, she is making ethical decisions. The same happens when doctors use ethical criteria to choose which patients should be prioritized in face of limited resources, or when people are treated fairly in job interview despite their color and gender. With the speedy development of intelligent technology, systems are more and more pervasive, and machines are also expected to make ethical decisions and act ethically. However, to ensure that, system developers must be aware of ethical principles. Moreover, such principles must guide development, thus being explicitly embedded in system engineering methods. In this talk, I argue that ontologies may play a crucial role in supporting awareness, besides being essential in the development of engineering practices leading ethical and trustworthy systems. In particular, I describe our own current efforts in the development of ontology-based requirements engineering and what that entails. We will talk about promising directions and results of this research and discuss what else is to be expected as next steps.

Fintech and Artificial Intelligence in Finance

Matei MOCANU

Nils Holgerson

Mohamed Faid

MSCA WP3 Research Seminar - Ethical Requirements for AI Systems

Online event

The 4EC doctoral training course "Reinforcement Learning in Digital Finance" offers a comprehensive exploration of reinforcement learning (RL), which is a powerful framework to support sequential decision-making under uncertainty.

Candidates will delve into the theoretical foundations of reinforcement learning, state-of-the-art algorithms, and practical applications within the digital finance domain. RL can facilitate and enhance sequential decision-making processes and services in finance, such as algorithmic trading, portfolio optimization, risk management, and fraud detection. Doctoral candidates will gain a solid understanding of the theoretical underpinnings of RL as well as practical experience in implementing reinforcement learning algorithms to solve real-world financial problems. The applications will consider constraints and regulations that concern privacy, algorithmic bias, and explainability.

By the end of the course, candidates will be equipped with the knowledge and skills to leverage reinforcement learning for optimizing control, enhancing performance, and improving services in the dynamic landscape of digital finance.

*Click [here](https://www.digital-finance-msca.com/schedule/msca-training-week-ut-1) to see the schedule of the event.*

Frederik Sinan Bernard

Michael R.

Wouter van Heeswijk

MSCA Training Week - UT

Ravelijn building, University of Twente

fintech-and-ai-in-finance

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