We invite contributors across five broad themes, welcoming perspectives from AI, HCI, and public policy.

Each theme is introduced by an illustrative question, but the questions and topics listed below are intended to serve as starting points rather than limits. We also encourage submissions that go beyond these themes by extending, reframing, or connecting them.

1. Safety and Alignment for Agentic & Physical AI

How do we ensure that increasingly autonomous and embodied AI systems remain safe, aligned, and under meaningful human control?

Possible topics: agentic and embodied AI, retrieval-augmented generation (RAG), multimodal alignment, human-in-the-loop, safety evaluation, LLM vulnerabilities, scalable oversight, uncertainty estimation and calibration, vision-language-action (VLA) models

2. Open Models, Data Governance, and Responsible Deployment

How can we build open and adaptable AI systems that avoid dependence on a small number of models, datasets, or service providers?

Possible topics: open-weight and foundation models, open data, data provenance and quality, local adaptation, benchmarking and evaluation, efficient training, compute and API dependencies

3. Human Agency & Trustworthy Human-AI Interaction

How can people effectively understand, oversee, and intervene in AI systems without losing their own agency?

Possible topics: human-AI and human-robot interaction, explainable AI(XAI), delegation, mixed-initiative interaction, appropriate reliance, usable transparency and privacy, user control, contestability, human intervention, and failure recovery

4. Inclusive AI for Underserved Communities

How can people from different technical, cultural, and professional backgrounds help build AI that better serves diverse languages, communities, and public needs?

Possible topics: multilingual and low-resource NLP, culturally grounded AI, community-driven data and models, accessibility, civic technologies, news and information integrity, media literacy, and resource-efficient deployment

5. Democratic Resilience and Multi-Stakeholder AI Governance

How can governments, researchers, industry, and the public meaningfully participate in governing AI and holding it accountable?

Possible topics: agile regulatory design, AI ethics, AI law and privacy, public accountability, transparency and explainability, auditable algorithms, due process and contestability, institutional coordination, public-sector deployment, co-governance, and deliberative democracy