Speaker Summer School 2026
Dr. Sayantan Auddy
Technische Universität Berlin
Hands-on Session
TBD
Bio >
Dr. Thorsten Eisenhofer
CISPA Helmholtz Center for Information Security
In-depths lecture with Hands-on: Trustworthy Agentic Systems
Agentic AI systems are moving from research prototypes to real-world software ecosystems, where they can access data, APIs, tools, and operational processes. While this makes complex technologies more accessible, it also introduces new security risks: every layer of the agentic stack can become an attack surface.
This session explores how autonomous AI systems are built, where security boundaries fail, and what researchers and practitioners may need to consider when designing more trustworthy agentic systems. In a mix of theoretical presentations and practical tasks, we will examine emerging attack vectors, architectural weaknesses, and strategies for securing agentic systems in sensitive and high-impact domains.
Bio >
David Hartmann
Weizenbaum-Institut e.V.
Input & Hands-on: What Does this Number Even Mean? A Critical Introduction to AI Benchmarks and Evaluation
Benchmarks and evaluations play a central role in determining what counts as "performant," "safe," and "responsible" AI and shape AI's use as a research instrument. This workshop treats benchmarks as epistemic artefacts and asks what their scores actually measure. Two short inputs introduce benchmarks as artefacts, their datasets, annotations, and design choices, and the notion of construct validity, the question of whether a measurement captures the concept it claims to. Participants then critically assess two benchmark types, for performance and for "AI safety," first reconstructing their underlying measurement model, then probing them directly. Participants systematically perturb inputs to see when and how scores break down. The workshop closes by drawing out the implications for validity, reproducibility, and reliability in participants' own research, and for how we should read AI leaderboards.
Bio >
Dr. Angelie Kraft
Weizenbaum-Institut e.V.
Input & Hands-on: What Does this Number Even Mean? A Critical Introduction to AI Benchmarks and Evaluation
Benchmarks and evaluations play a central role in determining what counts as "performant," "safe," and "responsible" AI and shape AI's use as a research instrument. This workshop treats benchmarks as epistemic artefacts and asks what their scores actually measure. Two short inputs introduce benchmarks as artefacts, their datasets, annotations, and design choices, and the notion of construct validity, the question of whether a measurement captures the concept it claims to. Participants then critically assess two benchmark types, for performance and for "AI safety," first reconstructing their underlying measurement model, then probing them directly. Participants systematically perturb inputs to see when and how scores break down. The workshop closes by drawing out the implications for validity, reproducibility, and reliability in participants' own research, and for how we should read AI leaderboards.
Bio >
Dr. Vince Madai
BIH Berlin Institute of Health
In-depths Lecture: Responsible Design of Autonomous AI Systems: From Ethical Principles to Practice
The rapid evolution of artificial intelligence is increasingly moving from passive decision-support tools toward more autonomous AI systems capable of interpreting complex information, generating recommendations, interacting with users, and initiating or coordinating actions. This development demands a renewed focus on responsible design to ensure that such systems are not only technically capable, but also ethically justified, robust, transparent, and socially trustworthy.
While broad consensus has emerged around principles for trustworthy and responsible AI, a major gap remains in translating these principles into concrete design choices, governance structures, validation practices, and implementation procedures. This challenge becomes especially urgent as autonomous AI systems are deployed in complex, high-risk real-world settings, where their outputs may influence human decisions, institutional workflows, access to services, allocation of resources, and accountability structures.
This talk explores how responsible AI can move from abstract ethical principles toward more concrete forms of practice. It will examine key challenges raised by autonomous AI systems. Healthcare will be used as an illustrative high-stakes example, but the broader focus is on responsible design considerations that are relevant across domains. The aim is to critically discuss what it means to operationalize ethical principles in the design, validation, deployment, and oversight of autonomous AI systems.
Bio >
Professor Dr. Peter Neubauer
Technische Universität Berlin
In-depths Lecture
TBD
Bio >
Professor Dr. Iyad Rahwan
Max Planck Institute for Human Development
Keynote: Science Fiction Science
Can we predict the social and behavioral impacts of future technologies, such as Artificial Intelligence, while they are still being developed in scientific labs, or even when they are just imaginations in the minds of a science fiction writer? Such prediction would allow us to guide development and regulation of technologies before their impacts get entrenched. This talk describes ‘science fiction science’ (sci-fi-sci), the use of experimental methods to simulate future technologies, and collect quantitative measures of the attitudes and behaviors of participants assigned to controlled variations of the future. I present various recent sci-fi-sci projects aimed at anticipating the societal impacts of Artificial Intelligence, and discuss the potential and limitations of this form of science.
Bio >
Dr. Lea Schönherr
CISPA Helmholtz Center for Information Security
In-depths lecture with Hands-on: Trustworthy Agentic Systems
Agentic AI systems are moving from research prototypes to real-world software ecosystems, where they can access data, APIs, tools, and operational processes. While this makes complex technologies more accessible, it also introduces new security risks: every layer of the agentic stack can become an attack surface.
This session explores how autonomous AI systems are built, where security boundaries fail, and what researchers and practitioners may need to consider when designing more trustworthy agentic systems. In a mix of theoretical presentations and practical tasks, we will examine emerging attack vectors, architectural weaknesses, and strategies for securing agentic systems in sensitive and high-impact domains.
Bio >
Professor Dr. Judith Simon
Universität Hamburg
Keynote
TBD
Bio >
Professor Dr. Marc Toussaint
Technische Universität Berlin
Keynote: Physical Intelligence
The term Physical Intelligence roughly refers to bringing AI into our physical world, e.g. in terms of robots. However, scientifically this means that AI methods -- which are mostly data-driven -- meet fundamental laws and constraints of physics, for which we often have concise models. This raises the interesting question on how our understanding of physics should and can be leveraged for physical AI, or whether AI should learn physics from scratch -- abandoning our scientific understanding. I will discuss challenges in physical intelligence from the perspective of robotics, and approaches to combine data-based and model-based methods.