This year, AIES is experimenting with a small number of co-located workshops external to the official conference but close to it in time and space. The intention is for conference attendees to have useful venues in which to have more specific discussions and exchanges than is appropriate at the full conference, and to foster new and interesting interactions within topical slices of the wide variety of subjects discussed at AIES.
Workshop: Converging Perspectives on Health AI
The Converging Perspectives on Health AI Workshop aims to provide a space for community building centered on the question:
How can we develop clinical AI systems that are technically robust, clinically useful, socially acceptable, and responsibly deployed? Or, put simply, how should we be using AI in real-world clinical settings?
https://cphai-2026.github.io/submit
Workshop: Responsible AI in times of crisis
Crises such as pandemics, armed conflicts, or earthquakes force decision-makers to act quickly under high stakes and deep uncertainty. This makes them a natural test case for AI-based decision support. But using AI here raises a prior question: does it actually improve decisions, or does it introduce new risks?
Crisis situations involve difficult trade-offs, and it is often unclear which trade-offs an AI system is making, whether it has the right information to make them, and whether decision-makers know what to expect from its advice.
In this workshop we will discuss how AI can be used responsibly in crisis situations. We will start off with a report of our experiences modelling policies for the COVID and Ebola crises. Based on those experiences, we will discuss the following four aspects:
- Data and models: Do we have the right information to create models or train AI systems for crisis settings?
- Guarantees and validation: What guarantees can be given on the output of the AI system?
- Preparedness and adaptation: Can we prepare systems before a crisis happens, and adapt them to the specific situation as it unfolds?
- Project organization: How do we organize the process of building an AI system during a crisis, under time pressure and with limited resources?