Christopher Danz

Let's embark on a journey to uncover and understand biases in AI. Detection is just the beginning. How do we ensure our data is as bias-free as possible? This presentation will provide practical strategies for exploring and cleansing our data and our models, setting the stage for more equitable AI development.

The core of our discussion will focus on mitigating biases. We’ll explore a range of techniques applicable at different stages of the machine learning lifecycle, from design to deployment. Attendees will learn not just how to identify and correct biases, but also how to proactively design systems that are inherently more fair and balanced.

This talk is more than a technical overview, it’s a call to action for developers, data scientists, and AI enthusiasts to excel fairness in AI. By the end of this session, participants will be equipped with the knowledge and tools to steer their AI projects towards more ethical, unbiased outcomes.

Generative AI is revolutionizing industries—from creating art and music to advancing natural language processing. But as these models grow more complex, they often come with a "black box" character, leaving us questioning how decisions are made. How can we trust and responsibly deploy technologies we don't fully understand?

Join us for an enlightening session designed for everyone—from curious newcomers and junior developers to seasoned AI practitioners and tech leads. We'll delve into the current landscape of Explainable AI (XAI) and Responsible AI (RAI) as they apply to Generative AI (GenAI) applications.

In this talk, we'll explore:

- The Importance of XAI and RAI to understand why explainability and responsibility are critical in AI applications.
- State-of-the-Art Methods: Get up to date on the latest techniques and standards used to make models more transparent and accountable.
- Industry Adoption: Real-world use-case how a company adopted XAI in GenAI applications