AI Policy Lab Day 2025: Highlights and Reflections (Recording Available)

2025-11-26 · Source: AI Policy Lab · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Public Policy & Governance, Emerging Technologies & Innovation · Depth: Advanced, quick

Summary

The AI Policy Lab Day 2025, held on November 19, 2025, at Umeå University, featured key discussions on responsible AI. Sennay Ghebreab's keynote introduced "Question Infinity," advocating for continuous reflection on AI's risks and opportunities rather than a static assessment. Daniel McQuillan presented "decomputing," proposing a framework of degrowth, conviviality, and care to address AI's systemic failures and prioritize collective well-being over speed. The event also showcased mature research posters from PhD candidates Rachele Carli, Petter Ericson, Jason Tucker, Tatjana Titareva, Themis-Dimitra Xanthopoulou, and Mattias Brännström, demonstrating their engagement with societal needs. The day concluded with a screening of "Humans in the Loop," highlighting the invisible labor and agency of data workers in India.

Key takeaway

For AI Ethicists and Policy Makers developing responsible AI frameworks, you should integrate a continuous, reflective process for assessing AI risks and opportunities, moving beyond static checkpoints. Your policies must also address the systemic failures and social realities underpinning AI, prioritizing collective well-being and acknowledging the human labor involved, as highlighted by the "Humans in the Loop" narrative.

Key insights

AI policy requires continuous reflection on risks and opportunities, integrating social realities and collective well-being.

Principles

• AI assessment is a continuous process.
• Confront AI's systemic failures.
• Prioritize collective well-being over speed.

Method

Decomputing combines degrowth, conviviality, and care to imagine responses that prioritize collective well-being over speed or scale in AI development.

In practice

• Consider "Question Infinity" for AI deployment.
• Examine material realities of AI labor.
• Integrate data worker perspectives.

Topics

• Responsible AI
• AI Ethics
• AI Policy
• Data Annotation Labor
• Decomputing

Best for: AI Ethicist, Policy Maker, AI Researcher

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Policy Lab.