Opening Remarks
Organiserat Bridging Prediction and Intervention Problems in Social Systems (Simons Institute, 2026) · 2026-01-12..01-18·
summary
Claims that predictive accuracy alone fails to predict downstream impact and that an integrated viewpoint is necessary, with a formal split between predictive risk and judgment vs predictive score (a policy indicator Z). Tools named are causal inference, HCI lab studies, benchmarking and field experiments for real-world effects. Evidence cited: mixed results across domains (sepsis, pre-trial tools, education) and widespread deployment (nine out of ten landlords use tenant screening). A collaborative white paper and university courses exist and report notable downstream influence.
AI-written summary · about
chapters (7)
- 0:00Welcome
- 1:19Workshop origins
- 4:09Automated decisions in social contexts
- 8:02First workshop impact
- 12:45Prediction and intervention framework
- 20:44Model design and evaluation
- 24:09Interdisciplinary collaboration
related talks
38mTo explain or to predict?
49mGetting to True Predictive Risk: Will Data Accuracy Thwart AI’s Potential?
43mBeyond Just Prediction: Causal Thinking in Machine Learning
43mIn defence of uncertainty
31mEthics in technology: History, practice, and mitigation
41mKeynote: Personalization, Bandits, and Causal inference
47mKeynote: Using Data Science for Social Good: Examples, Opportunities, and Challenges
29mResponsible AI Challenges and Recommendations