Jyotja Trevarenas

AI Summary

→ WHAT IT COVERS Professor Jutta Trevarenas explains how AI systems trained on statistical averages exclude outliers and minorities, proposing data exploration algorithms and radical inclusive codesign methods to create adaptive, resilient urban environments and products. → KEY INSIGHTS - **Data Exploration vs Exploitation:** Reverse AI algorithms to identify missing perspectives rather than replicate past success patterns. In employment systems, this creates diverse teams with multiple viewpoints instead of monocultures, providing more choices when crises require pivoting direction. - **Virtuous Tornado Design Process:** Design with excluded users first, then expand outward through iterations asking who remains excluded. This stress-tests solutions against catastrophic scenarios and unexpected needs, creating systems that work during crises when people lack capacity to adapt themselves. - **Outliers Drive Innovation:** The 20 percent of needs scattered at the periphery of human requirements represent vital few who experience system cracks first. Designing for their struggles creates flexible systems with room for change when mainstream users face unexpected challenges or crises. - **Radical Inclusive Codesign:** Recruit currently excluded people as critical experts who define problems and design processes alongside professionals. This builds invested communities organically, eliminates marketing needs, and creates natural placekeepers who maintain and adapt designs over time without additional effort. → NOTABLE MOMENT Automated vehicle engines tested in 2013 decided to proceed through intersections when encountering a wheelchair user moving backwards. After receiving more training data, the systems made the same dangerous decision but with increased confidence levels. 💼 SPONSORS None detected 🏷️ Inclusive Design, AI Bias, Urban Planning, Codesign Methods