Artificial Intelligence: Regulation, the Fundamentals of Risk Management, Personal Data
The course will provide you with the necessary knowledge on AI regulation, including the recently adopted EU AI Act. Apart from that, you’ll learn how to effectively assess and manage risks.
- August 20 - September 19
- 10 sessions
- Certificat upon successful completion of tests
- €1050 + VAT
About the course
The course offers an in-depth overview of the regulatory definitions and classifications surrounding Artificial Intelligence (AI). We provide a comparative analysis of various regulations, including OECD guidelines, EU requirements, and standards from NIST and ISO. This course is designed not only to help you identify what aspects of AI are regulated and why, but also to give you the overview of its different types, such as “strong” and “weak” AI. Additionally, we cover the primary methods and applications of Artificial Intelligence, making our course an excellent opportunity for both beginners and experienced professionals in the AI industry.
Moreover, our training focuses on the challenges and risks of AI, from individual to ecosystem levels. It offers comprehensive strategies and methods to navigate them, highlighting the essential features of AI systems and their responsible usage. We explore various regional and national regulatory frameworks and risk management structures. Additionally, we delve into the subject of personal data regulation and AI, discussing the principles of personal data processing and the potential risks that automated decision-making creates for individuals.
Why does it matter?
Data Protection Officers should take AI training to understand the risks associated with AI, protect data from breaches and leaks, comply with regulations, build customer trust, and drive development and innovation with privacy and data security in mind.
01.
The Evolution of Legislation and Peculiarities of Regulatory Documents
In the rapidly evolving AI world, we see the introduction of the first comprehensive AI regulation in the EU, along with regional legislative acts in the USA, Canada, UAE, Saudi Arabia, and beyond. This global shift presents a complex regulatory environment, demanding AI-operating businesses to not only adhere to various standards but also demonstrate adaptability to a spectrum of regulations.
02.
Financial and Economic Risks
Failure to comply with AI regulations can result in fines, sometimes based on company turnover, as seen in the EU. This could lead to significant financial instability for the company. Systematic violations might risk the closure of business operations (partially or fully). Complying with AI regulations is not just about avoiding penalties — it’s about ensuring sustainable business growth.
03.
Reputational Risks
A lack of transparency in customer data usage and non-compliance with ethical and legal standards can affect your business reputation. Being proactive about ethical and regulatory considerations from the outset of AI system development can enhance your market position in a long-term.
02.
Competive Advantage
The responsible development and usage of Artificial Intelligence plays a major role in building brand loyalty and customer trust.
What are we going to focus on?
01.
We will consider when and where artificial intelligence regulation applies to your company’s or customers’ activities and identify what to look out for in this context.
02.
We will discuss the necessary steps to assess all risks and effectively manage processes related to the creation or adoption of AI systems, including in the context of personal data processing — both at the stage of development of these systems and at the stage of operation.
03.
Not only that, but we’ll examine the differences in methods when it comes to AI regulation in different countries, with a focus on the European Union. Moreover, we’ll discuss strategies for companies operating in different jurisdictions.
Program
1.1 Classification, methods, and main applicability of Artificial Intelligence.
- “Strong” and “Weak” AI, general purpose and specialization.
- Rule-based systems and Machine Learning.
- Predictive and Generative AI.
- Large language and multimodal models (LLM).
1.2 Normative definitions and classifications.
- Comparative analyses (OECD, NIST, EU, Council of Europe, etc.): common elements of definitions and differences.
- What is regulated in the context of AI and why (high risk AI systems, systems requiring transparency).
2.1 Key challenges and risks at the various levels:
- Individuals (civil rights, economic opportunities, security).
- Social groups (discrimination).
- Society (democratic processes, public trust in public institutions, education, and jobs).
- Organizations (reputation, culture, profitability, competition, sustainable growth and development).
- Ecosystems.
2.2 Overcoming challenges and risks through the characteristics of systems and the surrounding processes.
- Key characteristics of reliable Artificial Intelligence systems (accuracy, robustness, security, etc.).
- Processes for responsible usage of AI, including effective human control and effective remedies.
3.1 Regional and national regulatory frameworks (NRAs), focusing on the EU AI Act.
- Requirements of the EU AI Act.
- Recommendation services, DSA requirements.
- CE Convention.
- Code of ethical recommendations.
3.2 Risk Management Frameworks (ISO 31000 + ISO 23894, NIST AI RMF, ForHumanity RMF)
3.3 Management frameworks — integration of procedures and controls for the development and operation of AI systems into business processes (ISO 42001 AI Management System).
4.1 The intersection of personal data regulation and AI:
- The use of personal data in the AI lifecycle, in particular for model training.
- Impact of the AI on the rights of personal data subjects.
4.2 AI and the principles of personal data processing:
- AI and lawfulness, fairness and transparency of processing.
- The principle of fairness of data processing and AI bias.
- AI explainability and algorithmic transparency.
- AI and purpose limitation: Data reuse and Test de compatibilité.
- Artificial Intelligence and data accuracy.
- AI data integrity and confidentiality.
4.3 Automated decision-making.
4.4 AI and DPIA (Data Protection Impact Assessment), HRIA (Human Rights Impact Assessment), AI Compatibility assessment.
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