Algorithms and datasets are not objective. They encode historical dynamics, cultural norms, institutional practices, and human biases—the values and assumptions of the people who created them.
#human-impact, #labor-market
Read the topic >Training and inference consume real energy and water. Measure the footprint of every model call before you decide to scale it up.
#environment, #sustainability
Read the topic >Where did the training data come from, and who consented to it? Traceable datasets are the foundation of accountable systems.
#transparency, #datasets
Read the topic >Large models are often overkill. Small language models can deliver the same civic-tech outcome at a fraction of the cost.
#efficiency, #slm
Read the topic >Affected communities need continuous mechanisms to shape and critique the AI systems being built for them.
#accountability, #community
Read the topic >Document tradeoffs openly so that non-experts can understand how a system will impact them and offer meaningful feedback.
#transparency, #reporting
Read the topic >Automation reshapes who does what work. Map the downstream effects on the people whose jobs your tool touches.
#labor-market, #human-impact
Read the topic >Accountable tech is usable tech. Bake in keyboard navigation, contrast, and assistive-tech support from the first commit.
#accessibility, #human-impact
Read the topic >Shipping fast with AI is easy; maintaining it is not. Plan for updates, dependencies, and failure modes up front.
#security, #maintenance
Read the topic >Reduce redundant prompting. Every unnecessary call adds latency, cost, and environmental load to your workflow.
#efficiency, #cost
Read the topic >A curated repository of civic-tech tools, hosted by the working group and made available open source for future builders.
#open-source, #community
Read the topic >Use the Seedscore framework to measure human, systems, and environmental impact across your AI-enabled build.
#accountability, #framework
Read the topic >