Fair AI

AI is rapidly transforming industries, but with innovation comes the critical need for responsible, compliant deployment. In this meetup we will cover key trends, timelines, and both internal and external factors for building compliant AI ecosystems.
We’ll break down the EU AI Act, explore its impact alongside GDPR, and offer practical guidance for aligning your AI efforts with emerging regulations. You'll gain clear insights on how to manage risks and make confident, informed decisions.

💡 You’ll Learn:
• The key provisions of the EU AI Act and their impact on AI development and deployment
• How to identify which AI systems fall under the regulation’s scope
• Common compliance challenges and how to address them proactively
• Essential questions to ask your AI, legal, and product teams to ensure alignment
• Strategic approaches to embed compliance into your AI governance and risk management

🧠 Who Should Attend?
• C-level executives and VPs steering AI strategy and innovation
• Product Managers and Heads of AI or Data Science
• Anyone responsible for AI adoption, governance, or regulatory oversight

This session will be clear, concise, and free of technical jargon, with ample opportunity for your questions.

Causal AI has quickly become a highly talked about concept in the AI community. Its promise: Equip systems with a deeper understanding of how the world actually works through cause-and-effect reasoning. Advocates claim it’s the next big leap for decision-making, generalization, and safety in AI.
But how much of this is hype and how much is real? And more importantly: what role could Causal AI actually play in improving today’s generative AI and agentic systems?
While large language models and agent-based architectures have shown impressive capabilities in generation, planning, and execution, they still struggle with grounding, consistency, and strategic foresight. Causal AI offers a potential solution but it also brings new complexity, strong assumptions, and practical limitations.
In this session, we’ll critically explore how these two paradigms:“Causal AI“ and „Generative AI“ differ, where they struggle, and how they might work together to enable more intelligent, autonomous, and trustworthy agentic workflows.

💡 You’ll Learn:

• The core differences between Causal AI and Generative AI: capabilities, strengths, and limitations
• Why generative systems struggle with reasoning, and where causal thinking can fill the gap
• How Causal AI can improve planning, grounding, and reliability in Agentic systems
• When generative models outperform causal ones and when they fail
• Strategic considerations for integrating causal reasoning into generative AI workflows

🧠 Who Should Attend?

• C-level executives and VPs exploring advanced AI strategies
• Product Leaders and Innovation Executives shaping AI capabilities
• Data and Analytics Leaders responsible for delivering trustworthy insights
• Teams already using RAG who want to push performance to the next level
• Anyone responsible for critical decision-making powered by AI

The conversation will be high-level and jargon-free, with plenty of time for Q&A.