Tessel Haagen
Consultant Data & AI and Trainer at Info Support B.V.
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Tessel is a consultant in data & AI at Info Support, specialized in interpretable AI. In her daily work, she applies AI-augmented engineering to build and evaluate intelligent systems, or gives trainings about AI. She is known as an enthusiastic knowledge source and an insatiably curious lifelong learner. Outside of work, she channels her strategic thinking into board games (with a soft spot for complex strategy games) and escapes into fantasy novels. With an MSc in Artificial Intelligence, an MSc in Computer Science, and an MA in Linguistics, she combines a strong technical foundation with a deep interest in language and reasoning.
Panel: When AI Feels Empathic
Panel moderator: Richard Campbell
Panellists: Michelle Frost, Martine Dowden, Tessel Haagen
There’s a word for what a person feels when someone truly listens to them, then understands and shares their experience and perspective. There isn’t one for what happens when a language model does it.
We’ve been borrowing terms from psychology that don’t quite fit, and then defaulting to technical terms that don’t reach far enough. In the space between, we’re shipping systems that produce and evoke something real in people: attachment, relief, dependency, maybe genuine help. And we’re doing it without the language to describe what that actually is, or any shared understanding of what it should require from the developers and technologists making those
decisions.
This gap is showing up in the lives of people who are already vulnerable, as well as in real moments of benefit we would be wrong to dismiss. As an industry, we don’t have this figured out. Neither does anyone on this panel. We have different backgrounds, different experiences and instincts, things we will genuinely agree or disagree on, and we think this conversation belongs here, with the people who are actually shipping our future.
Part 2/2: From Hallucination to Justification: Hands-On Explainability for LLMs
Human beings are biased and often wrong. AI learns from human-created data. Therefore, AI is biased and often wrong. This has been a critical problem across machine learning applications in the last years. To break open the black box of AI models, and understand how they make decisions, the concept of explainability was introduced.
Then, LLMs entered the chat. They answer our questions confidently and with a beautiful prose, even when they are making up data. Explainability then becomes essential to trust -or not- their output. But when the existing explainable AI methods cannot be directly applied to these models, what do we do?
In this workshop, we will delve into explainability and its importance in the current context of LLMs and agents. Starting from traditional ML to then focus on LLMs, we will cover the different methods that can be implemented, from well-known ones to novel proposals stemming from our internal research. We will also introduce research-proven prompting strategies, tips, and tricks to integrate explanations on third-party LLM services that are not natively explainable.
Through guided exercises, you will get to peek under the hood of AI models, LLMs' behavior, and agents reasoning, by trying out these different techniques, and seeing their benefits and limitations first-hand. You will experience the risks and challenges that generative AI and agentic AI bring when implementing explainability, and learn practical ways to tackle them.
NDC Oslo 2026 Sessionize Event Upcoming
WeAreDevelopers World Congress 2026 - Europe Sessionize Event
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