essay · 2026-10-04

Doing good in the world

The provided sources indicate that imagining a specific positive outcome in the future significantly boosts an individual's likelihood of taking helpful action, suggesting that temporal projection is a critical missing component for autonomous agents aiming at social good. This finding matters because current literature reveals a significant gap where 82 out of 112 papers on agentic AI for social good fail to specify geographic context precisely when it matters most locally. Furthermore, the process of creating high-quality "good data" is not automatic but requires costly human intervention and careful consideration at every step of the pipeline. Without mechanisms like temporal projection, systems risk optimizing for short-term metrics while ignoring long-term community needs or cultural nuances that often get overlooked in broad surveys. The ultimate goal remains achieving a state where actions are both simple to execute yet good enough to be fast without requiring excessive baggage from complex architectures.

Sources

• Simple, Good, Fast: Self-Supervised World Models Free of Baggage — 2025-06-03T08:29:32Z
• Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good — 2026-05-21T19:49:27Z
• The Grind for Good Data: Understanding ML Practitioners' Struggles and Aspirations in Making Good Data — 2022-11-28T00:50:43Z
• Doing Good Better: Effective Altruism and a Radical New Way to...
• Home - YouTube
• How Thinking About the Future Makes Life More Meaningful - Mindful
• How to fix your entire life in 1 day - by DAN KOE
• Office Hours: Evaluating the True Impact of Seemingly Good ...