Week 3 journal entry

This week gave me a more nuanced understanding of AI. I had considered it more of a technical artifact than something that was so deeply influenced through institutions and incentives. I hadn't seen data (therefore AI) depicted as a concentrated form of power that can set defaults for everyone else. Klein's emphasis that AI's mystery and objectivity talk can protect companies from accountability stood out as particularly interesting. The Nemer & Sobral make labor central (AI as "heteromation"), dependent on hidden infrastructure instead of autonomous intelligence. This gave a solid answer for the "where does AI come from?" question. It comes from distributed workers, platform governance, and other factors, not just from engineers. What I felt was still unaddressed/partially addressed was how to create resistance within institutions. Participatory approaches sound promising, but I am wondering whether that will be a quick or effective enough approach. It also leaves questions about what mechanisms (labor standards, research funding structures, ect.) actually force institutions to adopt them? What does accountability look like when harms are global and take a long time to show (environmental costs/long term surveillance normalization)?

I'm also interested to see what changing the default settings looks like in practice. If power is concentrated in states/corporations/institutions then individual ethical intentions feel insufficient. What is the most effective approach or angle? Would something like unionization or public pressure be a better focus than policy or transparency mandates? More concrete examples of communities that meaningfully altered an AI system's deployment/design.