Smol Gardens began as a concept presented by the Femmecubator team at BetaNYC's UnSchool of Data 2026 conference on March 2026, emerging from their Open Civic Tech initiative. The second workshop was presented at Makeshift 2026: Accountable Tech by Design event during NYC Design Week.
The project's name draws inspiration from Anil Dash's 2012 talk "The Web We Lost," which critiqued the internet's evolution into isolated, walled gardens. Today, as AI reshapes the technological landscape, Smol Gardens invites civic technologists to challenge LLM platform usage and explore alternative AI tools in civic tech tool building.
Designers, builders, and civic technologists are facing a core problem: how to develop civic tech responsibly in the age of AI without creating redundant, unmaintainable, or harmful solutions.
Is it possible to create meaningful work using LLM models? When a new wave of vibecoders (builders with no programming skills) are shipping websites in less than a day, there's a clear gap in accountability that needs to be addressed.
Smol Gardens establishes a groundwork for iterative study through three phases:
Create the impact assessment tracking tool. We're developing a framework that measures human, systems, and environmental impact on AI-enabled web-development.
Invite civic technologists document actual AI usage through a "diary mission," using the Impact assessment tool as guide.
Use small language models (SLMs) to build a curated repository of civic tech tools hosted by the Accountable Tech working group and will be made available open source for future builders.
This workshop is for civic technologists who are already using AI tools in their workflow. Participants will be invited to document and audit their work using an impact assessment framework. The study aims to educate and push for alternatives to using LLM models, with an end goal to incorporate Accountable tech principles in a civic builder's workflow.
Responsible computing recognizes that technology is NOT neutral. Every algorithm, dataset, system design, and infrastructure choice reflects human power structures, cultural values, and societal assumptions. Our role as technologists is to:
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.
Technical projects affect different groups unequally. Marginalized and vulnerable populations often experience disproportionate harm. Critical questions to ask:
Every technical decision involves value judgments. Trace these choices through hardware architecture, software implementation, and data science models. Make choices visible and contestable.
Ethical dilemmas cannot be solved through quantitative metrics alone. Bring qualitative tools from STS (Science, Technology, and Society) alongside technical analysis. Avoid isolated decision-making and engage:
The Human Contexts & Ethics (HCE) Toolkit provides analytical concepts to systematically examine where human choices are embedded in technology:
Use this checklist to systematically apply responsible computing principles:
Responsible computing is not a compliance checklist or a box to check before launch. It is a disciplined practice of making visible the human choices embedded in technology, analyzing their consequences for different groups, and determining where you can intervene to build systems that are more equitable, transparent, and accountable.
This work addresses a fundamental question: Accountability in Tech, specifically with the process of vibecoding or rapid prototyping with AI. Vibecoding offers accelerated development and efficiency gains, yet it presents a complex tradeoff. Can we harness its benefits while mitigating its harms?
Can we build responsibly in the Age of AI?
Ethical AI policy and legislation are trailing far behind the speed of deployment. The technology is already being misused — malvertising via vibecoded sites, unauthorized use of writers' and designers' work, and with new products promising to be a job killer for designers, diminishing the value of human creative labor.
Large orgs see AI-powered development as a cost-cutting alternative, one that could replace one thousand interns. Yet the reality is more complicated. Data centers are proliferating to support a trillion-dollar LLM industry, creating staggering environmental costs. When we apply second-order thinking, deeper issues emerge: the process introduces redundancy, creates maintenance burdens, raises data sovereignty concerns, and opens vectors for malware.
The computational demands of training large language models carry a significant environmental burden that is often overlooked. Training a single common AI model can emit more than 626,000 pounds of CO2 equivalent, approximately five times the lifetime emissions of an average car, including manufacturing.
This environmental cost raises critical questions: if we're building AI solutions to serve communities responsibly, can we justify deploying models with such substantial carbon footprints for applications that smaller, more efficient models might adequately serve? The environmental impact of AI development is not merely a sustainability concern; it represents a failure of accountability to future generations and disproportionately affects communities already vulnerable to climate impacts, making responsible model selection a matter of environmental justice in civic tech.
Source: UNESCO's Recommendation on the Ethics of Artificial Intelligence
We brainstormed with 15 attendees and captured themes from their feedback on this issue:
The original discussion notes are posted on this document.
This framework is recommended for civic tech builders to evaluate their workflow using a the IAT (Impact Assessment Tool) scorecard that contains 15 questions. The scorecard uses three core pillar categories: Human, Environment and Systems Impact.
Below is how a project might be assessed across key criteria:
Did participants gain capability, ownership, and transferable knowledge through this process?
Is the solution still aligned with the civic problem it was designed to solve — or has it drifted toward efficiency for its own sake?
Did building this tool expand participants' technical, collaborative, or domain capabilities?
Did the team accumulate cognitive debt — outsourcing reasoning to AI in ways that leave no one able to explain or maintain decisions?
Did participants make deliberate choices about which tools to use, or did they default to AI suggestions?
Could this tool harm, exclude, or manipulate any member of the community it serves?
Did we build something communities can own, maintain, and trust without depending on us?
Is the methodology documented and shareable so other builders can build on it without starting from scratch?
Were real-world harms understood and documented before the tool went live?
Does the community retain meaningful control without depending on platforms they don't own?
Is there a maintenance plan sufficient for the tool to survive without its original builders?
Can end users understand how the tool works and why it produces the outputs it does?
Have the safety risks and privacy implications for the community been identified, documented, and mitigated?
Did we take responsibility for the environmental cost of the AI we used?
Did we track and document the carbon emissions generated by our inference calls?
Did we deliberately choose the smallest model capable of the task and log the number of inference calls made?
Did we account for the full resource cost — energy, carbon, and water — and publish our findings for others to replicate?
Does the energy infrastructure powering this AI displace resources or burden communities — local or future — who had no say in that choice?
The Accountable AI 30-Day Challenge is an intensive, collaborative experiment bringing together civic tech builders, designers, and community members to build meaningful tools using ethical AI practices.
Choose one of two civic domains and develop a real project that addresses a community need:
| Phase | Days | Focus |
|---|---|---|
| Discovery | 1–5 | Problem identification, community interviews, POV statement writing |
| Ideation | 6–12 | Concept sketching, tool selection, architecture planning |
| Build | 13–25 | Prototype development, user testing, iteration cycles |
| Polish & Document | 26–30 | Finalization, environmental audit, report writing, presentation prep |
| Role | Responsibilities | Commitment |
|---|---|---|
| Lead Facilitator | Overall coordination, orientation facilitation, final report authorship | Full-time, 30 days |
| Participants (5–10) | Project selection, building, daily documentation, evaluation, design crit | ~2–3 hrs/day |
| Crit Reviewers (3–5) | Attend final design critique, provide written feedback | Days 28–30 only |
| Open-Source Community | Challenge first drafts, contribute to the Smol Gardens repo. | Ongoing / voluntary |
Responsible computing recognizes that technology is NOT neutral. Every algorithm, dataset, system design, and infrastructure choice reflects human power structures, cultural values, and societal assumptions. Our role as technologists is to:
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.
Technical projects affect different groups unequally. Marginalized and vulnerable populations often experience disproportionate harm. Critical questions to ask:
Every technical decision involves value judgments. Trace these choices through hardware architecture, software implementation, and data science models. Make choices visible and contestable.
Ethical dilemmas cannot be solved through quantitative metrics alone. Bring qualitative tools from STS (Science, Technology, and Society) alongside technical analysis. Avoid isolated decision-making and engage:
The Human Contexts & Ethics (HCE) Toolkit provides analytical concepts to systematically examine where human choices are embedded in technology:
Use this checklist to systematically apply responsible computing principles:
Responsible computing is not a compliance checklist or a box to check before launch. It is a disciplined practice of making visible the human choices embedded in technology, analyzing their consequences for different groups, and determining where you can intervene to build systems that are more equitable, transparent, and accountable.