🌱 CharityScribe AI Environmental Impact Statement
CharityScribe is committed to the responsible and ethical use of artificial intelligence. We recognize that AI technologies — including generative models and cloud‑based systems — require significant amounts of computational resources, which can contribute to greenhouse gas emissions, energy consumption, and water use. For example, modern AI data centers can have electricity footprints comparable to tens of thousands of households and rely on cooling systems that use billions of gallons of water annually.
At CharityScribe, we take a balanced, responsible approach:
1. Acknowledge the environmental footprint of AI.
AI infrastructure — from training large models to serving millions of queries — consumes energy and resources. We acknowledge this and transparently work to minimize unnecessary computation and wasteful processes whenever possible. We do this by optimizing our agents for cost and performance, so that we use as few resources as possible to generate the highest quality output. We don’t encourage AI use for the sake of AI use.
2. Partner with sustainability‑minded platforms and technologies.
We choose AI service partners and infrastructure providers who publicly commit to energy efficiency and renewable energy goals. Industry leaders such as NVIDIA are advancing energy‑efficient GPU architectures and accelerating computing methods that can lower total energy use for given workloads.
3. Promote efficient usage rather than wasteful scaling.
Rather than training new massive models from scratch for every task, CharityScribe uses optimized, pre‑trained models and techniques that reduce redundant computation and cut energy costs. This aligns with best practices for Green AI, where systems and workflows are designed to deliver value with lower environmental impact.
4. Support nonprofits in maximizing impact per watt.
CharityScribe accelerates key nonprofit work — like writing grants, producing communications, translating content, and analyzing data — meaning organizations can achieve more impact with less energy, staff time, and resource use. This helps nonprofits focus finite resources on mission, rather than development tasks.
5. Align with responsible AI community values.
We are inspired by mission‑driven partners that explicitly demonstrate that ethical, values‑aligned AI usage — at small scales relative to big tech — can be far more sustainable in aggregate.
CharityScribe strives to be transparent about the environmental trade‑offs of AI and to continuously improve our practices in line with evolving sustainability standards and emerging Green AI methodologies.
🌿 CharityScribe Green AI Framework
Mission: To reduce the environmental impact of CharityScribe’s AI-powered platform while maintaining effectiveness, affordability, and accessibility for nonprofits.
🌱 Guiding Principles
Efficiency over scale: Prioritize efficient models and workflows over brute-force scaling.
Transparency: Disclose key decisions related to energy use, infrastructure, and sustainability.
Responsibility by design: Embed sustainability in product and engineering decisions from day one.
Impact per watt: Measure success not just in usage, but in positive nonprofit outcomes per unit of energy consumed.
🧩 4-Part Framework for Green AI at CharityScribe
1. Optimize AI Infrastructure & Model Usage
Use pre-trained foundation models where possible instead of retraining from scratch.
Choose providers with public sustainability goals and renewable energy use.
Leverage low-carbon regions for hosting when selecting cloud services.
Monitor and reduce idle or redundant API calls — apply caching, batching, and context reuse.
2. Measure and Report Environmental Footprint
Implement energy-use tracking per user task or workload (partnering with cloud providers where needed).
Develop an internal Carbon Efficiency Score: emissions per dollar of nonprofit value delivered.
Publish an annual Environmental Impact Summary.
Adopt standard metrics from Green AI research (e.g., carbon intensity per model call, FLOP counts, etc.)
3. Design for Sustainability and User Impact
Prioritize product features that maximize mission-aligned efficiency.
Nudge users toward lower-impact workflows where possible (e.g., text summarization vs. full rewriting).
Offer nonprofits visibility into their own usage and footprint (optional “Sustainable AI Mode”).
Avoid over-engineering — simplicity and clarity over novelty when it saves energy.
4. Engage Partners, Funders & Community
Partner with platforms and providers to align on best practices.
Share our progress and roadblocks openly — become a sector leader in Green AI for social good.
Collaborate with sustainability-focused foundations and grantmakers on AI/impact evaluations.
Advocate for nonprofit tech infrastructure that supports low-carbon tools and policies.
