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19.12.2024

Workshop: AI Leadership and Trustworthy AI in Practice

The IQZ Workshop "Transparency in AI: From Bias Awareness to Documentation Practices" at IQZ Kaiserslautern launches a workshop series on the trustworthiness of AI systems. In a time when AI is increasingly being used in critical areas such as medicine, undetected decision biases represent one of the biggest obstacles to their safe use. The European AI Act responds to this with comprehensive transparency requirements for AI providers. But it's not just developers who are challenged – users also benefit from a thorough understanding of transparency aspects when selecting and integrating AI solutions. This workshop provides a practice-oriented introduction and teaches best practices for the safe implementation and documentation of AI systems.

What to expect:

  • In-depth insights into opportunities and challenges of AI documentation

  • Practical best practices for implementing and selecting trustworthy AI systems

  • Updates on transparency requirements of the European AI Act

  • Strategies for detecting and handling bias in AI systems

The workshop will be conducted in English. Participation is free of charge.

Event Details:

  • Time: January 30, 2025, 1:45 PM – 5:30 PM

  • Location: Innovation & Quality Center, German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern

  • Target Audience: Executives, entrepreneurs, as well as AI developers and users from industry

  • Speakers: Jana Fehr (Berlin Institute of Health at Charité), Ludger van Elst, David Dembinsky, Hiba Najar (all DFKI Kaiserslautern)

Agenda

01

TAI 101: A Brief Introduction to Trustworthy AI

  • Trustworthy AI Basics: Building a Common Ground

02

AI Transparency for Trustworthiness

  • Unmasking Bias: Dangers & Pitfalls

  • Exploring the Meaning of AI Transparency

  • Best Practices for Transparent AI through Documentation

  • Effective AI Documentation – State-of-the-Art 

03

Transparency Beyond Documentation

  • Overview on Challenges & Opportunities of Explainable AI

  • Importance of Quality Metrics

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