Ethics in AI and Digital Finance Training [MSCA DIGITAL EVENT]
FinNex Academic Spinoff is proud to host the MSCA Doctoral Training on Ethics in AI and Digital Finance in Oradea, Romania, January 20th to 22nd 2025.
This training week is part of the MSCA Digital Industrial Doctoral Network, an initiative funded by the prestigious Marie Skłodowska-Curie Actions, dedicated to training highly skilled doctoral candidates in cutting-edge aspects of digital finance and artificial intelligence.
DIGITAL MSCA provides specialized training for young researchers in cutting-edge Research and Development topics in alignement with Industry Partners that span the diverse disciplines essential to the fast-evolving field of Digital Finance.
This program significantly exceeds the scope of traditional Finance PhD education by addressing a broad spectrum of inter-sectoral applications, including data quality, Artificial Intelligence (AI) and Machine Learning (ML), Explainable AI (XAI), blockchain innovations, and sustainable finance.
Lecturers
Agenda
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Welcome Speech – WP9 Leads
Lecturers: Sanda Filip (Vice-Rector University of Oradea), Cristian Miheș (Dean Faculty of Law Oradea), Dorin Coita (Vice-Dean FSE Oradea), Wouter van Heeswijk (University of Twente, MSCA WP7 Lead), Catarina Simões da Silva & Ioana Coita (MSCA – WP9 Leads), Marius Vlad Pop (FinNex Doctoral Researcher)
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9:00 – 11:00
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Introduction to Ethics (1/2)
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Location: Biblioteca Universității, University Of Oradea 📍Directions
Lecturer: Frédérik Sinan Bernard (University of Twente)
This intensive course provides a comprehensive introduction to ethical theories and their critical application within the realms of mathematics, statistics, and data science and beyond, in social sciences. Designed specifically for PhD students and faculty members of the MSCA DIGITAL FINANCE network, the 2-part lecture bridges foundational philosophical concepts with contemporary issues in technology and finance.
Focus Areas: Financial Data Space (Ethical Considerations: Data privacy, consent, and ownership; Philosophical Lens: Kant’s duty-based ethics emphasizing respect for individuals’ rights; Application: Balancing the need for data in research with stringent privacy regulations like GDPR), Artificial Intelligence for Financial Markets (Ethical Challenges: Market manipulation, algorithmic bias, and transparency; Philosophical Lens: Utilitarianism and Rawls’ theory of justice to assess the broader societal impact;
Application: Developing fair AI models that promote equitable financial opportunities), Explainable and Fair AI( Ethical Imperative: Transparency and accountability in AI; Philosophical Lens: Aristotle’s virtue ethics highlighting honesty and integrity; Application: Crafting AI algorithms that are not only effective but also interpretable and just), Driving Digital Innovations with Blockchain (Ethical Dilemmas: Privacy vs. transparency, environmental sustainability; Philosophical Lens: Balancing utilitarian benefits with Kantian duties to society; Application: Innovating blockchain solutions mindful of ethical trade-offs), Sustainability of Digital Finance (Ethical Focus: Long-term social and environmental consequences; Philosophical Lens: Aristotle’s concept of the common good and Rawls’ principles of fairness; Application: Promoting responsible fintech developments that align with ESG criteria).
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Coffee Break
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Ethical guidelines for AI models
Lecturer: Catarina Silva (University of Coimbra)
This session explores ethical guidelines for AI models, focusing on principles such as fairness, transparency, accountability, and privacy. Participants will examine real-world case studies and regulatory frameworks to understand the challenges and best practices in developing and deploying ethical AI systems. The session aims to equip researchers with tools to navigate ethical dilemmas and design AI models that align with societal values while fostering trust and minimizing risks.
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Lunch Break
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Ethicality Requirements Elicitation and Analysis
Lecturer: Renata Guizzardi (University of Twente)
As systems increasingly impact societal norms, ensuring ethicality in their design and implementation has become a critical concern. This course explores the foundational principles, methods, and frameworks for eliciting and analyzing ethicality requirements in technology-driven projects. Students will engage with interdisciplinary perspectives, including ethics, engineering, and behavioral science, to understand the complexities of translating abstract ethical principles into actionable system requirements.
Key topics include stakeholder identification, the role of biases in ethical decision-making, regulatory and compliance considerations, and techniques for managing ethical trade-offs in system design. Through case studies, practical exercises, and collaborative projects, participants will develop skills to systematically capture ethical concerns and integrate them into the requirements engineering process.
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Coffee Break
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Ethics for Financial Analysts
Lecturer: Stefan Theußl (Research Innovation Hub of Raiffeisen Research at RBI Group)
In the rapidly evolving landscape of AI and digital finance, ethical considerations are paramount for financial analysts. This talk delves into the core ethical principles outlined by the EFFAS Principle of Ethical Conduct [1], as well as the CFA Institute Code of Ethics and Standards of Professional Conduct [2], emphasizing the imperative to act with integrity, competence, diligence, and respect. Financial analysts are called to prioritize the interests of clients and the integrity of the investment profession above personal gains, exercising independent professional judgment and promoting ethical behavior. These principles become increasingly crucial as analysts begin to interact with AI technologies, both as tools for drafting assessments and as interfaces for client engagement.
Networking Dinner
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Data Science in a mathless Digital Society
Lecturer: Wolfgang Karl Härdle (Humboldt-Universität zu Berlin)
In an increasingly digitalized world, the demand for actionable insights from data has never been greater. Yet, paradoxically, we find ourselves in a society where mathematical literacy is often sidelined in favor of intuitive, user-friendly interfaces. This talk explores how data science can thrive in a “mathless” digital society, where the emphasis shifts from technical expertise to accessibility and democratization. We will examine the role of automation, machine learning, and no-code tools in bridging the gap between complex algorithms and practical decision-making. Case studies will illustrate how these advancements empower individuals and organizations to harness the power of data without requiring deep mathematical knowledge. The talk also addresses the risks of oversimplification and the need for ethical frameworks to guide the responsible use of data-driven technologies.
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XAI for Finance
Lecturer: Daniel Pele (University of Economics in Bucharest)
In the rapidly evolving field of artificial intelligence, transparency and trust are critical for ensuring the ethical and effective deployment of AI systems. This course, Introduction to Explainable AI (XAI), provides a foundational understanding of the principles, methods, and tools used to make AI models interpretable and explainable to diverse stakeholders.
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Cognitive biases – from humans to machines
Lecturer: Xiaohong Huang (University of Twente)
Cognitive biases, the systematic deviations from rational decision-making, shape human judgment and behavior in profound ways. As artificial intelligence and machine learning systems increasingly influence critical decision-making processes, understanding how these biases manifest in machines is essential. This course delves into the origins, mechanisms, and impacts of cognitive biases, exploring their transition from human cognition to algorithmic design and application.
Students will examine key cognitive biases such as anchoring, confirmation bias, and availability heuristics, investigating their implications for human behavior and the risks they pose when embedded in AI systems. Topics include bias detection and mitigation strategies, the interplay between biased data and algorithms, and the ethical considerations of designing bias-aware systems. Case studies from diverse fields such as healthcare, finance, and social media will provide real-world context to theoretical insights.
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Coffee Break
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Ethicality Requirements Elicitation and Analysis
Lecturer: Renata Guizzardi (University of Twente)
As systems increasingly impact societal norms, ensuring ethicality in their design and implementation has become a critical concern. This course explores the foundational principles, methods, and frameworks for eliciting and analyzing ethicality requirements in technology-driven projects. Students will engage with interdisciplinary perspectives, including ethics, engineering, and behavioral science, to understand the complexities of translating abstract ethical principles into actionable system requirements.
Key topics include stakeholder identification, the role of biases in ethical decision-making, regulatory and compliance considerations, and techniques for managing ethical trade-offs in system design. Through case studies, practical exercises, and collaborative projects, participants will develop skills to systematically capture ethical concerns and integrate them into the requirements engineering process.
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AI Act – regulation & compliance
Lecturer: Cristian Mihes (Faculty of Law – University of Oradea)
The AI Act is a significant piece of legislation aimed at regulating artificial intelligence (AI) within the European Union. It establishes a comprehensive legal framework to ensure the development and deployment of AI systems are safe, transparent, and aligned with fundamental rights. The Act categorizes AI systems based on risk levels, with stricter regulations for high-risk applications and bans on those deemed unacceptable, such as social scoring by governments.
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Lunch Break
Visit to Oradea Musem
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Implications and future scenarios about the use of GenAI in banking and society
Lecturer: Renato Rocha Souza (Research Innovation Hub of Raiffeisen Research at RBI Group)
The presentation will cover an introduction to large language models (LLMs), highlighting their capabilities and the transformative potential they hold for various industries. We will briefly discuss the concept of Artificial General Intelligence (AGI) and the future scenarios it might enable, emphasizing the profound societal changes that could arise. Additionally, we will explore the potential of Generative AI (GenAI) to revolutionize creative industries, content generation, and personalized experiences. We will address the pervasive issue of biases in LLMs and their ethical ramifications. As AI systems become increasingly integrated into various sectors, the biases inherent in LLMs can lead to unfair or discriminatory outcomes. We will examine the sources of these biases, including training data and algorithmic design, and their potential impacts on marginalized communities. By fostering a dialogue on responsible AI practices, we aim to raise awareness and promote the creation of more equitable and trustworthy AI systems.
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Diversity aspects in Banking/ Fintech
Lecturer: Ioana Popa (Banca Transilvania)
As we look ahead, the AI enabled future is not anymore about tech, or STEM as we used to know them. It is about creativity, about shaping the technologies for their best of use. Education plays a key role in this transformation. By encouraging young women, or experienced professionals to thrive using these tools, we can ensure that technologies of tomorrow embed diverse needs, perspectives and enable deeper expertise.
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Ethical Boundary in AI-Based Risk and Scoring Models in retail banking industry – study case
Lecturer: Ionut Verzea (FinNex)
The era of artificial intelligence (AI) in risk evaluation is reshaping the financial sector, surpassing the limitations of traditional scoring models that relied on limited data categories. With technological advancements enabling the processing of larger datasets with greater accuracy and speed, the potential to develop more efficient and secure risk assessment systems has never been greater.
Networking Dinner
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Explainable Reinforcement Learning
Lecturer: Wouter van Heeswijk (University of Twente)
In the talk “Explainable Reinforcement Learning,” we delve into the explainable AI techniques that are applicable to sequential decision making in finance. The presentation begins by outlining foundational explainable AI techniques, proceeds with techniques tailored to reinforcement learning in the computer science domain, and ends with the green field of explainable reinforcement learning in finance.
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Introduction to Ethics (2/2)
Lecturer: Frédérik Sinan Bernard (University of Twente)
This intensive course provides a comprehensive introduction to ethical theories and their critical application within the realms of mathematics, statistics, and data science and beyond, in social sciences. Designed specifically for PhD students and faculty members of the MSCA DIGITAL FINANCE network, the 2-part lecture bridges foundational philosophical concepts with contemporary issues in technology and finance.
Focus Areas: Financial Data Space (Ethical Considerations: Data privacy, consent, and ownership; Philosophical Lens: Kant’s duty-based ethics emphasizing respect for individuals’ rights; Application: Balancing the need for data in research with stringent privacy regulations like GDPR), Artificial Intelligence for Financial Markets (Ethical Challenges: Market manipulation, algorithmic bias, and transparency; Philosophical Lens: Utilitarianism and Rawls’ theory of justice to assess the broader societal impact; Application: Developing fair AI models that promote equitable financial opportunities), Explainable and Fair AI( Ethical Imperative: Transparency and accountability in AI; Philosophical Lens: Aristotle’s virtue ethics highlighting honesty and integrity; Application: Crafting AI algorithms that are not only effective but also interpretable and just), Driving Digital Innovations with Blockchain (Ethical Dilemmas: Privacy vs. transparency, environmental sustainability; Philosophical Lens: Balancing utilitarian benefits with Kantian duties to society; Application: Innovating blockchain solutions mindful of ethical trade-offs), Sustainability of Digital Finance (Ethical Focus: Long-term social and environmental consequences; Philosophical Lens: Aristotle’s concept of the common good and Rawls’ principles of fairness; Application: Promoting responsible fintech developments that align with ESG criteria).
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Ethics in Social Science Research with a Focus on Experimental Economics
Lecturer: Jana Peliova (University of Economics in Bratislava)
The convergence of digital finance with ethical and sustainability considerations is reshaping the financial landscape, presenting both opportunities and challenges. The talk aims to provide a comprehensive understanding of how ethical principles and sustainability objectives can be integrated into digital finance, guiding the sector towards responsible innovation and long-term value creation.
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Coffee Break
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Transparency and Bias in Financial Models
Lecturer: Karsten Wenzlaff (University of Hamburg) & Olivija Filipovska (Geostrategic Institute Global Skopje)
The rapid advancement of digital technologies has fundamentally transformed the financial sector, introducing both unprecedented opportunities and complex ethical challenges. This talk, titled “Ethics in Digital Finance,” will explore the multifaceted ethical considerations that arise from the integration of digital innovations in financial services. The talk aims to provide a comprehensive understanding of the ethical landscape in digital finance, offering insights into how financial institutions can navigate these challenges to foster trust and integrity in the digital age.
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Embedding Corporate Digital Responsibility in Financial Services Organisations
Lecturer: Adriana Tiron-Tudor (University of Babes-Bolyai in Cluj)
As digital transformation reshapes the financial services landscape, organizations face growing expectations to act responsibly in their use of technology. Corporate Digital Responsibility (CDR) encompasses a commitment to ethical, sustainable, and accountable practices in the design, deployment, and governance of digital systems. This course provides a comprehensive framework for understanding and integrating CDR principles into financial services organizations.
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Ethics in AI for tax research tool – application
Lecturer: Matilda Barrow (Thomson Reuters)
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Lunch Break
Responsible AI – Working session – Reflection on research projects
Lecturer: Catarina Silva (University of Coimbra)This dynamic session is designed to foster creativity and collaboration between aspiring researchers and seasoned academics. Students will have the opportunity to present their innovative ideas in a 5-minute pitch, followed by 5 minutes of constructive feedback from an esteemed panel of professors and industry experts. Following the pitches, the focus will shift to a round-table discussion among professors and experts, centering on the broader challenges and ethical considerations associated with the field. Topics may include the practical hurdles of implementing innovative ideas, navigating ethical dilemmas, and scaling academic research for real-world applications.
Transparency, Diversity, and Non-Discrimination in Data – study case
Lecturers: Codruta Mare, Liana Stanca (University of Babes-Bolyai in Cluj)
Diversity has become one of the most important aspects in the strategies of the European Union. It is a sensitive and complex side that needs special approaches, especially nowadays, in the AI Era. The goal of this presentation is to clarify the relationship between diversity and transparency to ensure fairness. At the foundations one can find the DATA. Non-discriminatory data substantially contributes to fulfilling the fairness goal, contributing to trustworthy AI.
Networking Dinner
Registration
REGISTRATION CLOSED
Thank you for your interest in attending the Ethics in AI and Digital Finance Training School, Romania, 2025.
On-line attendance is available using the link below:
Join the onsite waiting list
However, PhD students and AI researchers may join the waiting list for onsite participation by filling out the form available below:
MSCA Digital Doctoral Training Hosts in Oradea [RO]
Our Partners
Beneficiaries
Associated Partners
Network Members
This event is supported from: Fellowship (FinClusion_ FEBA_2023) Leveraging alternative data sources for building a sustainable and unbiased credit scoring tool National Scientific Program “Petar Beron i NIE” in Bulgaria, BG-175467353-2023-14-0004 – 2023
Fellowship (WiseCredit) Integrating Personality Traits and Open Banking Data for Sustainable and Ethical Creditworthiness Assessments, Fellowships for Excellent Researchers R2–R4 RRP Slovakia 09I03-03-V04-00502- 2023
Disclaimer
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Horizon Europe: Marie Skłodowska-Curie Actions. Neither the European Union nor the granting authority can be held responsible for them.
This project has received funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101119635
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