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World Congress 2026 Europe - Virtual Stage • Jul 2, 2026 • Session details

Introduction to Responsible AI: Balancing Value and Risk

Seppe Housen

AI systems aren't built—they're grown. Discover how to overhaul your software development lifecycle with red teaming to balance generative AI value against unpredictable risks.

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#1 about 2 min

Recognizing artificial intelligence as a statistical superpower

Early exposure to statistics highlights the incredibly magical ability of models to extrapolate future outcomes like sales or complex fraud.

#2 about 3 min

Highlighting valuable artificial intelligence applications today

Breakthroughs like AlphaFold and modern coding assistants demonstrate the profound time-saving value of current AI systems.

#3 about 4 min

Learning from real-world artificial intelligence failures

Negative outcomes arise when AI is rolled out incorrectly, as seen with hallucinated speeches, rogue agentic automation, and biased fraud detection.

#4 about 2 min

Balancing value and risk for responsible artificial intelligence

Building responsible AI requires acknowledging explicit new opportunities for automation alongside the acceleration of severe environmental and societal risks.

#5 about 2 min

Starting with the software development life cycle

Methodologies like Agile and the V-model provide a foundational structure for engineering high-quality technology solutions from scratch.

#6 about 2 min

Understanding how artificial intelligence logic is grown

Because systems learn from examples rather than relying on explicit deterministic rules, the underlying development cycle requires extensive systemic adaptation.

#7 about 5 min

Strengthening cross-functional controls and risk management

Sustaining accurate AI products demands robust organizational controls and cross-domain collaboration to manage fairness, safety, and strict regulatory compliance.

#8 about 7 min

Adding specific validation steps for generative and predictive models

Unlike traditional deterministic software, probabilistic generative systems lack clear ground truth and rely heavily on specialized validation testing prior to broad user rollout.

#9 about 6 min

Iterating extensively through evaluations and red teaming

Managing unpredictable trade-offs between accuracy, latency, and privacy means products must loop iteratively through structural evaluations, rigorous red teaming, and guardrail implementation.

#10 about 2 min

Committing to new methodologies for transforming technology

Safely harnessing the transformative capacities of neural networks necessitates adapting system lifecycles to accommodate rigorous testing and iterative architectural design.

Matching moments

5:18 min

Addressing psychological safety and ethical risks of AI adoption

Vera Slavnić Vera Slavnić · Europe 2026 Virtual

1:34 min

Introduction to responsible artificial intelligence and societal impact

Toju Duke · WWC 2022

1:47 min

Evaluating ethical responsibilities for AI product integration

Krzystof Czieslak · LIVE

1:31 min

Integrating regulatory policy into responsible artificial intelligence

Rebekka Weiss Rebekka Weiss +1 · WWC 2025

3:08 min

Understanding the landscape of AI capabilities and risks

Balázs Kiss · WWC 2023

3:08 min

Implementing responsible artificial intelligence frameworks to mitigate model bias

Alexander Wallner Alexander Wallner +3 · WWC 2024

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