AI in Finance 2400-MIKRO-AIBF-AIF

Key Focus Areas:
- The UK &EU AI Landscape: Explore the current state, growth trends, key players, and government initiatives.
- AI in Finance: Examine applications like chatbots and LLMs across front, middle, and back office operations.
- AI Risk Management: Learn to identify and mitigate risks such as bias, privacy, concerns, and unintended consequences.
- Ethical & Legal Foundations: Delve into philosophical considerations, focusing on autonomy, the right to explanation, and value alignment.

Topic 1 Introduction to AI This session will demystify Artificial Intelligence and lay the groundwork for understanding its crucial ethical dimensions. We'll start by breaking down core AI concepts, including machine learning and deep learning, and trace their evolution over time. You'll gain a clear understanding of how these technologies work and how they've transformed various industries. Ethical Principles in AI Next, we'll dive into the fundamental ethical principles that should guide AI development and deployment.
We'll explore:
- Fairness, transparency, accountability, and non-maleficence as cornerstones of responsible AI.
- Strategies for recognizing and mitigating bias – whether it stems from data, algorithms, or human oversight.
- The critical importance of human control in AI systems, with practical examples drawn from chatbots and Generative AI (GenAI) applications.
- AI Ethical Frameworks.
Finally, we'll examine established ethical frameworks that provide a lens for analyzing AI's societal impact: ✓ An exploration of frameworks like utilitarianism and deontology. ✓ An investigation into how virtue ethics applies to AI and the concept of Digital Identity.

Topic 2. Application of AI in Finance This session will explore the practical implementation of AI within the financial sector, from real-world applications to strategic adoption roadmaps. Real-World Applications & Ethical Dilemmas We'll begin by examining diverse applications of AI in finance, including fraud detection, and delve into case studies such as Kenshoo Technologies AI Innovation HUB for S&G and the FCA AI Sandbox (Kaizen). We will also analyze ethical dilemmas encountered in AI deployment, using examples like Barclay's AI initiatives and Teaching Chatbot tools. We'll showcase how platforms like Bloomberg are utilized in real-world financial settings (DS functions) and Refinitiv's expansion with Microsoft to launch market-data- powered AI assistants in Microsoft Teams.

Topic 3: Risk Management in AI This session will focus on identifying, assessing, and mitigating the diverse risks associated with Artificial Intelligence.
1. Identifying AI Risks We'll categorize potential risks into three key areas: ✓ Technical Risks: Examining challenges like model bias and data privacy breaches. ✓ Societal Risks: Discussing broader impacts such as job displacement, the implications of autonomous weapons, and the dangers of deepfakes. ✓ Economic and Financial Risks: Addressing threats like financial crimes and cybercrimes enabled or exacerbated by AI.
2. Risk Assessment and Mitigation Strategies Learn how to proactively manage AI risks: ✓ Quantifying Risks: Developing methods to measure and assess the severity of AI- related risks. ✓ Effective Countermeasures: Designing and implementing strategies to mitigate identified risks. ✓ Regulatory Compliance & Governance Frameworks: Understanding the landscape of AI regulation, including the UK's AI Regulatory Principles, the EU AI ✓ Act, and the U.S. Artificial Intelligence Policy. ✓ Real-World Case Studies: Analyzing actual incidents of AI-related risks and their consequences, such as Air Canada's chatbot misleading information, OpenAI's ChatGPT hallucinations, and Microsoft chatbot racist comment incidents.

Topic 4: AI, Legal, Governance, and Leadership This session will delve into the critical aspects of governing AI, establishing leadership, and ensuring responsible investment. 1. AI Governance and Oversight This section focuses on establishing robust frameworks for managing AI: ✓ Establishing Clear Roles and Responsibilities: Defining who is accountable for AI ✓ strategy, development, and implementation within an organization. ✓ Creating Governance Structures: Building oversight mechanisms to manage AI ✓ initiatives and ensure ethical and legal compliance. ✓ Case Studies: The role of a Chief AI Officer (CAIO) in leading AI initiatives. The importance of AI ethics committees in reviewing and approving AI projects. 2. AI Investment and Resource Allocation Strategizing for effective AI adoption: ✓ Prioritizing AI Initiatives: Identifying AI projects with the most promising benefits and strongest alignment with organizational goals. ✓ Strategic Resource Allocation: Distributing resources to AI initiatives based on their priority and anticipated return on investment. 3. AI and Sustainability Responsible AI Practices: Exploring the crucial intersection of AI development and deployment with broader responsible business practices and long-term sustainability goals.

The project ‘Application of Artificial Intelligence in Business and Finance’ (No. BPI/SPI/2024/1/00078) is implemented by the University of Warsaw as part of the Spinaker programme organised by the National Agency for Academic Exchange (within the project ‘Wsparcie tworzenia i realizacji międzynarodowych programów kształcenia’, funded by the European Funds for Social Development 2021–2027).