AI for Right Livelihoods

Summary

The AI for Right Livelihoods project will develop an open-source AI platform to help individuals discover meaningful, values-aligned work—including regenerative, community-centered, and entrepreneurial roles—and connect them to funding pathways that make these opportunities economically viable. Many people face barriers to meaningful employment, while critical social and ecological roles remain underfunded or invisible to traditional job markets. Our platform flips the conventional model: it starts with the worker’s skills, passions, and values, and uses AI to recommend pathways that align economic, social, and ecological outcomes.

The system leverages large language models and machine learning to analyze participant inputs and generate personalized recommendations, while our AI Integrity Checker ensures transparency, fairness, and ethical guidance. The platform will be piloted with 500+ participants in underserved populations, including low-income individuals, youth, women, caregivers, and workers in transition, as well as with nonprofits supporting workforce development.

By the end of the 18-month pilot, we expect participants to access meaningful work that increases income by $5,000–$7,000 per year on average, while nonprofits gain a scalable, AI-powered tool to improve efficiency and outcomes. The platform and documentation will be released as open-source resources, enabling replication and adaptation in communities worldwide. This initiative demonstrates a practical, technology-enabled pathway to Right Livelihoods, expanding economic opportunity while supporting regenerative and socially valuable work.

For GitLab:

The AI for Right Livelihoods project will develop an open-source AI platform that helps individuals identify meaningful, values-aligned work—including regenerative, community-centered, and entrepreneurial roles—and connect to funding pathways that make these opportunities economically viable. Using large language models and machine learning, the platform will analyze skills, aspirations, and local opportunities to generate personalized recommendations, with an integrated AI Integrity Checker ensuring fairness and transparency. In an 18-month pilot, we aim to reach 500+ underserved participants, increasing income by $5,000–$7,000/year on average while expanding access to socially and ecologically valuable roles. The platform will be released as open-source, enabling nonprofits and communities worldwide to scale these pathways to Right Livelihoods.

Budget Overview: AI for Right Livelihoods

The $250,000 grant will fund the development, pilot, and open-source release of the AI for Right Livelihoods platform over 18 months. Major allocations include:

  1. Platform Development & AI Integration ($120,000)

    • Building the recommender engine using large language models and machine learning.

    • Integrating the AI Integrity Checker for transparency, fairness, and ethical guidance.

    • Designing a user-friendly interface and community adoption tools.

  2. Pilot Implementation & Evaluation ($80,000)

    • Recruiting and supporting 500+ participants across 1–2 communities.

    • Collecting feedback, refining AI recommendations, and measuring outcomes (income improvement, placement in regenerative work).

    • Conducting impact assessment and reporting.

  3. Staffing & Consulting ($35,000)

    • Project management, AI/ML consulting, and technical support.

    • Training community partners and nonprofits in platform use.

  4. Open-Source Release & Documentation ($15,000)

    • Preparing code, manuals, and resources for public adoption.

    • Ensuring documentation supports scalability and replication in other communities.

This budget balances development, implementation, evaluation, and open-source dissemination, ensuring that the project produces measurable economic and social impact while creating a sustainable, scalable tool for expanding Right Livelihoods.

Here’s a cohesive draft grant narrative for your $250,000 proposal, combining all sections we’ve developed into a polished starting point for the GitLab Foundation AI for Economic Opportunity Fund application:

Center for Regenerative Community Solutions (CRCS) / Possible Planet

Mission:
At CRCS, also known as Possible Planet, we accelerate the transition to a just and sustainable world by developing community-based solutions to climate, ecological, and social challenges. We believe every community can thrive by aligning human livelihoods with ecosystem health and the well-being of future generations. Our initiatives—from advancing clean energy finance to fostering regenerative economies—create pathways for people to participate meaningfully in building a habitable, flourishing planet.

Project Overview: AI for Right Livelihoods

Goal:
Develop an open-source AI platform that helps individuals discover meaningful, values-aligned work—including regenerative, community-centered, and entrepreneurial roles—and connect them to funding pathways to make these opportunities economically viable.

Rationale:
Millions of people face barriers to meaningful work, while critical ecological and social roles remain underfunded or invisible to traditional job markets. Existing employment systems prioritize employer demand, leaving gaps in access to regenerative and socially valuable roles. Our platform begins with the worker—their skills, passions, and values—and uses AI to uncover pathways that align economic, social, and ecological outcomes.

Stakeholders:
Underserved and underrepresented populations, including low-income individuals, youth, women, caregivers, and workers in transition, as well as nonprofits and community organizations supporting workforce development.

AI/ML Integration

  • Recommendation Engine: Large language models and machine learning analyze participant skills, aspirations, and community opportunities to generate personalized work pathways.
  • AI Integrity Checker: Ensures transparency, fairness, and ethical guidance in all recommendations.
  • Continuous Learning: Algorithms refine recommendations based on participant feedback and observed outcomes.

Scope & Milestones

  1. Research & Design (Months 1–3): Map skills, opportunities, and funding; define AI architecture.
  2. Prototype Development (Months 4–7): Build recommender engine and user interface; integrate AI Integrity Checker.
  3. Pilot Testing (Months 8–12): Deploy with 500+ participants in 1–2 communities; refine recommendations using feedback.
  4. Evaluation & Open-Source Release (Months 13–18): Measure impact on income mobility and placement; release platform and documentation for broader adoption.

Intended Outcomes

Who?

  • Low-income individuals and households
  • Youth entering the workforce
  • Women and caregivers
  • Workers in transition
  • Community organizations and nonprofits

How Many?

  • Pilot phase: 500+ participants over 18 months
  • Future scale: thousands annually as open-source adoption grows

What Impact?

  • Increased income of $5,000–$7,000 per participant per year on average
  • Placement into meaningful, regenerative, or entrepreneurial roles
  • Improved alignment between participant skills, passions, and opportunities
  • Enhanced capacity of nonprofits and community programs through AI-driven tools

Change to Existing Programs:
The platform is designed to partner with existing programs—job placement, youth leadership, and workforce initiatives—to improve efficiency, reach, and placement outcomes while providing open-source tools for ongoing adoption.

Data Sources

  • Public taxonomies (O*NET, ESCO) for skills and occupations
  • Funding databases for grants, public programs, and cooperative opportunities
  • Community-contributed data (skills, values, local knowledge) collected with explicit consent and anonymized as needed
  • Accessibility: Data is open or available under standard agreements; no significant legal or privacy barriers exist.

Budget Overview ($250,000)

  1. Platform Development & AI Integration ($120,000)
    • Recommender engine, NLP, ML algorithms, AI Integrity Checker, user interface
  2. Pilot Implementation & Evaluation ($80,000)
    • Recruit 500+ participants, collect feedback, measure outcomes
  3. Staffing & Consulting ($35,000)
    • Project management, AI/ML consulting, training for partner organizations
  4. Open-Source Release & Documentation ($15,000)
    • Code, manuals, and resources for public adoption

Vision

AI for Right Livelihoods demonstrates a practical, technology-enabled pathway to economic opportunity that expands access to meaningful work, supports regenerative and community-centered roles, and creates scalable, open-source tools for nonprofits and communities worldwide. By combining AI with a worker-centered approach, the initiative addresses urgent societal and ecological needs while providing measurable economic benefits to underserved populations.

The original dialogue:

Given what you know about me and my interests, should I apply for the GitLab Foundation for AI for Economic Opportunity Fund, and if so what sort of idea should I pitch? I’m interested in open-source, commons-based models, especially for nonprofits, to build a better world for all. Could this somehow be used to help people with Right Livelihoods (see https://ppbook.shbn.net/part-4-conclusions/chapter-15-right-livelihoods-making-it-through-the-next-ten-years/)? Is there a way that the AI Integrity Checker can be positioned as an ecnomic opportunity? Given the GitLab’s https://www.gitlabfoundation.org/our-journey/ai-economic-opportunity-fund-inaugural-grantees and https://www.gitlabfoundation.org/our-journey/gitlab-foundation-announces-new-ai-for-economic-opportunity-fund-grantee-cohort, is there an obvious “missing” that stands out? Or would it be better to focus on other opportunities, since this one clearly seems to imply building a company to develop a product? Finally, considering the idea that there is no shortage of work that is needed, is it possible to conceive of a system that could interrogate potential applicants and then recommend meaningful occupations even if these are not currently funded, and then seek funding for them? Think of it as a local community employment center that works from the job-seeker to identify possible opportunities (including entrepreneurial opportunities or roles that are not currently paid but can demonstrate tangible ecological value) rather than from the employer-defined pool of available jobs?

I reviewed GitLab Foundation’s inaugural AI for Economic Opportunity Fund grantees. Their focus is tightly on AI that produces direct, measurable income mobility—tools that guide workers into higher-paying jobs, improve access to aid, or enhance employment systems.

What’s missing:

  • None focus on Right Livelihoods, ecological work, or commons-based economic opportunity.
  • None emphasize open-source, nonprofit-first AI models.
  • Few (if any) address the problem of meaningful but unpaid work—roles that have ecological or social value but lack market recognition.

How you could position yourself:

  • AI for Right Livelihoods: A system that helps individuals identify meaningful, socially/ecologically valuable occupations—whether paid, entrepreneurial, or grant-funded.
  • AI Integrity Checker as economic tool: Position it as infrastructure for trustworthy AI in the nonprofit/commons sector, enabling more orgs to adopt AI safely and equitably, expanding opportunity without exploitation.
  • Community employment recommender: Flip the model: start from the job-seeker’s skills, passions, and values, then identify viable pathways (including new regenerative roles), and connect them to funding streams (philanthropy, public programs, cooperatives).

Caveat: GitLab’s framing does lean toward “build a product, scale it, show economic ROI.” If you pitch, you’d need to frame your project as delivering measurable income uplift—perhaps by showing how Right Livelihoods or regenerative roles can be funded and converted into viable livelihoods through AI-enabled matching.

👉 So: yes, you could apply, but only if you cast your idea as an AI-enabled employment and livelihood recommender for regenerative and nonprofit economies—filling a real gap in their portfolio.

Would you like me to draft a pitch concept that connects Right Livelihoods + AI Integrity Checker + measurable income mobility into one coherent proposal for GitLab?

Yes

Perfect — here’s a draft pitch concept you could adapt for the GitLab Foundation’s AI for Economic Opportunity Fund. I’ve woven together your interests in Right Livelihoods, open-source, commons-based models, and the AI Integrity Checker, while framing it in their language of “income mobility” and measurable opportunity.

Pitch Concept: AI for Right Livelihoods

Tagline: Unlocking meaningful, regenerative work for all through open-source AI.

The Problem

Millions of people are trapped between two unsatisfying options: precarious low-wage jobs or unemployment. Meanwhile, there is no shortage of urgent work needed—restoring ecosystems, strengthening communities, supporting care networks—but these roles are often unfunded, invisible to job markets, and disconnected from workers’ skills and passions.

Current employment platforms are employer-driven, limiting opportunity to what is already profitable. What’s missing is a system that begins with the worker—their skills, aspirations, and values—and matches them to meaningful livelihoods, including new forms of regenerative and socially valuable work that can be funded through philanthropy, public investment, or cooperative ventures.

The Solution: AI for Right Livelihoods

We propose to build an open-source AI recommender system that:

  1. Interrogates job-seekers’ skills, passions, and values to identify meaningful career pathways, including traditional jobs, entrepreneurial opportunities, and regenerative roles not currently recognized by markets.

  2. Maps these opportunities to funding streams—from grants and public programs to cooperative and community-based financing—so that meaningful work can become livelihood-sustaining.

  3. Integrates an AI Integrity Checker to ensure recommendations are transparent, bias-aware, and aligned with worker well-being rather than exploitation.

  4. Publishes results as commons-based tools, enabling nonprofits, municipalities, and local employment centers to adopt the platform freely.

Why It Matters

  • Economic Opportunity: Expands the definition of opportunity beyond employer postings to include new, funded pathways for regenerative and community-centered work.

  • Income Mobility: Helps underserved workers move into sustainable, values-aligned livelihoods rather than dead-end jobs.

  • Equity & Inclusion: Builds a system that is accountable, open-source, and designed for nonprofits and marginalized communities.

  • Scalability: Localized versions could power community employment hubs worldwide, democratizing access to meaningful work.

The Gap We Fill

Unlike existing grantees, this project:

  • Focuses on open-source, commons-based AI (not proprietary platforms).

  • Expands economic opportunity into Right Livelihoods and regenerative roles—a missing dimension in today’s AI job-matching tools.

  • Provides a trust layer (AI Integrity Checker) to ensure recommendations are safe, transparent, and trustworthy.

Outcomes

  • Pilot deployment in 1–2 communities, matching at least 500 individuals to pathways that increase income or create sustainable livelihoods.

  • Documentation and open-source release of the platform.

  • A framework for scaling to nonprofit networks, municipalities, and global commons initiatives.

This frames your project as a social innovation that GitLab is not currently funding, but that clearly aligns with their mission.

Would you like me to condense this into a one-paragraph “executive summary” that could serve as the opening pitch in an application?

GitLab: Concept Note: AI for Economic Opportunity – Demonstration & Scaling

Here’s a draft mission statement for Center for Regenerative Community Solutions (CRCS) / Possible Planet, crafted to flow naturally into the executive summary of the AI for Right Livelihoods project:

Our Mission
At the Center for Regenerative Community Solutions (CRCS), also known as Possible Planet, we work to accelerate the transition to a just and sustainable world by developing community-based solutions to climate, ecological, and social challenges. We believe that every community has the potential to thrive by aligning human livelihoods with the health of ecosystems and the well-being of future generations. Our initiatives—from advancing clean energy finance to fostering regenerative economies—are designed to create pathways for people to participate meaningfully in building a habitable and flourishing planet.

This mission naturally leads us to our newest project: AI for Right Livelihoods, an open-source platform that helps individuals discover meaningful, regenerative work and connects them to the funding streams and support needed to make these pathways economically viable.

Would you like me to now draft the executive summary paragraph (as you’d use in a grant application) right after this mission statement, so the two flow seamlessly?

Yes

Great — here’s a polished executive summary paragraph that flows directly after the mission statement I drafted for CRCS / Possible Planet:

Executive Summary
We propose to develop AI for Right Livelihoods, an open-source platform that expands economic opportunity by helping individuals identify meaningful, values-aligned work—including regenerative roles that are vital for society and ecosystems but often invisible to the job market. Unlike traditional employment systems that start with employer demand, our approach begins with the worker—their skills, passions, and aspirations—and uses AI to map these to career pathways, entrepreneurial opportunities, and community-based roles. The platform integrates our AI Integrity Checker to ensure transparency, accountability, and fairness, while also linking workers to funding streams such as grants, public programs, and cooperative ventures. By reframing opportunity around Right Livelihoods, this project creates measurable pathways to income mobility while unlocking the vast pool of urgently needed but underrecognized work required to build a sustainable and regenerative future.

Would you like me to also condense this into a 2–3 sentence “funding pitch blurb” (the kind foundations often ask for at the start of an application), or keep it at this more detailed level?

Briefly summarize how artificial intelligence (AI), machine learning (ML), large language models (LLMs) or related advanced data science technologies will be used to advance your project goals.

Here’s a concise draft tailored for a grant application response to that question:

Use of AI/ML/LLMs
Our project leverages large language models and advanced machine learning to create an open-source Right Livelihoods recommender system. The platform will use AI to: (1) analyze individual skills, values, and aspirations through conversational assessment; (2) match users to meaningful work pathways, including regenerative and community-based roles not captured by traditional job markets; and (3) connect these opportunities to relevant funding streams. We will integrate an AI Integrity Checker to ensure recommendations are transparent, unbiased, and aligned with worker well-being, enabling nonprofits and communities to adopt trustworthy AI tools for expanding economic opportunity.

Would you like me to prepare a more technical version (highlighting methods like natural language processing, knowledge graphs, explainability frameworks) in case GitLab expects deeper detail on the AI stack?

Scope of Work. Provide a brief summary of project scope. Please include major milestones and specifically how you hope AI or related technologies will be used to advance these milestones.

Here’s a draft Scope of Work for your project, integrating milestones and AI use:

Scope of Work

The AI for Right Livelihoods project will develop an open-source platform to help individuals discover meaningful, values-aligned work and connect to funding pathways for regenerative and community-centered roles. The project will proceed in four major phases:

  1. Research & Design (Months 1–3)
    • Conduct stakeholder interviews and surveys to map skills, aspirations, and local/regional opportunities.
    • Define data architecture and AI model requirements.
    • AI/ML use: Employ natural language processing (NLP) to analyze participant inputs and classify skills, values, and potential work categories.
  2. Prototype Development (Months 4–7)
    • Build an initial AI-driven recommender engine linking user profiles to potential pathways, including paid, entrepreneurial, and grant-funded roles.
    • Integrate the AI Integrity Checker to ensure fairness, transparency, and bias mitigation.
    • Develop a user-friendly interface for community adoption.
  3. Pilot Testing (Months 8–12)
    • Deploy prototype in 1–2 communities, engaging at least 500 participants.
    • Collect feedback on recommendation relevance, usability, and economic outcomes.
    • AI/ML use: Refine recommendation algorithms using participant feedback, improving accuracy and alignment with individual aspirations.
  4. Evaluation & Open-Source Release (Months 13–18)
    • Evaluate project impact on income mobility, participant satisfaction, and regenerative work engagement.
    • Publish the platform and documentation as open-source, enabling adoption by nonprofits, municipalities, and cooperative networks.
    • AI/ML use: Implement final explainability and reporting features, ensuring transparency in recommendations and actionable insights for both users and administrators.

This phased approach ensures the project produces a scalable, measurable, and trustworthy AI platform that can expand economic opportunity through meaningful, regenerative work.

If you like, I can also create a very condensed 3–4 sentence version suitable for the GitLab form, keeping the milestones and AI integration but making it more concise. Do you want me to do that?

Yes

Here’s a concise version of the Scope of Work suitable for the GitLab application form:

Scope of Work (Concise Version)
The project will develop an open-source AI platform to help individuals identify meaningful, values-aligned work—including regenerative and community-centered roles—and connect them to funding pathways. Key milestones include: (1) research and design to map skills, aspirations, and opportunities; (2) prototype development with AI-driven recommendations and integrated integrity checks; (3) pilot testing with 500+ participants to refine accuracy and usability; and (4) evaluation and open-source release for broader adoption. AI/ML technologies, including NLP and recommender algorithms, will drive personalized matching and continuous improvement.

If you want, I can also draft the “Anticipated Outcomes / Impact” section in the same style so your application is fully aligned. Do you want me to do that?

First let’s address this: “Data sources. Briefly summarize the data sources you will be leveraging using AI related tools and how accessible this data is to your organization. (i.e., Is it stored in a way that is readily accessible, or are there any significant legal, privacy or regulatory concerns around access and use of this data?)”

Here’s a draft response tailored to your project and its AI use:

Data Sources
The AI for Right Livelihoods platform will leverage a combination of publicly available, open, and community-contributed data sources. These include:

  1. Skills and occupation taxonomies (e.g., O*NET, ESCO) to classify worker capabilities and potential pathways.
  2. Funding opportunity databases (grants, public programs, cooperative initiatives) that are publicly accessible and updated regularly.
  3. Community-contributed data from participants themselves, including skills, interests, values, and local knowledge of unmet ecological and social needs.

All data will be stored and managed by our organization in compliance with privacy standards. Participant-contributed data will be collected only with explicit consent, anonymized when possible, and securely stored to prevent unauthorized access. There are no significant legal, regulatory, or proprietary restrictions on the core data sources, and all sources are either open or will be made accessible under proper data-sharing agreements.

If you want, I can also draft a slightly more technical version showing how AI/ML models will interact with these data sources while maintaining privacy and compliance, which often strengthens a grant application. Do you want me to do that?

Under “Intended Outcomes,” Who? Define who your stakeholders are (e.g., women, youth, formerly incarcerated, low-income households, individuals living under the poverty line, etc).

Here’s a draft for the “Who?” section of Intended Outcomes, tailored to your project:

Intended Stakeholders
The AI for Right Livelihoods platform is designed to serve underserved and underrepresented populations who face barriers to meaningful work. Key stakeholders include:

  • Low-income individuals and households seeking stable, sustainable livelihoods.
  • Youth and young adults entering the workforce, especially those lacking access to career guidance or funding.
  • Women and caregivers looking for flexible, values-aligned employment opportunities.
  • Workers in transition or formerly unemployed who want to shift into regenerative or community-centered roles.
  • Community organizations, nonprofits, and cooperatives that support these populations and aim to implement AI-powered employment tools.

By focusing on these stakeholders, the project seeks to expand access to meaningful, values-aligned work while building capacity in organizations that facilitate economic opportunity and ecological regeneration.

I can also draft the “What?” and “How?” sections of Intended Outcomes next, so the full outcomes framework is ready for your application. Do you want me to do that?

What Impact? Briefly explain what specific outcome will they experience because of your project (e.g. undergraduate degree completion, increased income, job placement above a living wage, etc) and the scale of change that they will experience relative to their baseline or comparable population group (e.g. 20% increase in annual income, $500 per month income increase, 80% job placement rate, 25% improvement in graduation rates). E.g. We expect our clients to receive job placement that improves their income by $6,000 a year on average.

Here’s a draft for the “What Impact?” section of Intended Outcomes, tailored to your project:

What Impact?
Participants in the AI for Right Livelihoods pilot are expected to experience tangible improvements in income and economic stability by accessing meaningful work aligned with their skills, values, and community needs. We anticipate:

  • Job placement or income-generating opportunities that increase participants’ earnings by an average of $5,000–$7,000 per year compared with baseline income.
  • Access to new or underrecognized pathways in regenerative, community-centered, or entrepreneurial roles that were previously unavailable to them.
  • Increased career alignment and satisfaction, improving long-term workforce retention and engagement in socially or ecologically valuable work.

By targeting underserved populations, this represents a substantial relative improvement over comparable peers, creating both immediate economic benefits and longer-term pathways for sustainable livelihoods.

Next, we can draft “How?” (the mechanism by which these impacts are realized via AI and related tools) to complete your Intended Outcomes section. Do you want me to do that?

Change to an existing program? If the grant focuses on improving the efficiency or scale of an existing program, also state the intended outcomes associated with those improvements (e.g., reducing cost per individual reached by 50%; increasing the number of students reached per year from 500 to 1,000; improving the job placement rate from 80% to 90%, increasing completion rates by 20%) — I prefer to say that we are open to assisting other organizations in their job-placement, youth-leadership, etc. programs, and may well be able to improve the effciency or effectiveness of those programs

Here’s a draft response for the “Change to an Existing Program?” section, reflecting your approach:

Change to an Existing Program?
While AI for Right Livelihoods is primarily a new platform, we are open to partnering with existing organizations and programs—including job-placement services, youth leadership initiatives, and nonprofit workforce development programs. By integrating our AI-driven recommender system and the AI Integrity Checker, we aim to improve the efficiency and effectiveness of these programs, for example:

  • Reducing the time and resources needed to match participants with meaningful work.
  • Increasing the number of participants served annually.
  • Improving placement rates into sustainable, values-aligned roles.
  • Enhancing alignment between participants’ skills, passions, and available opportunities.

Through these collaborations, our platform can amplify the impact of existing programs while also generating insights and open-source tools that benefit the broader network of workforce and community development organizations.

If you like, I can now draft the “Anticipated Outcomes / Intended Outcomes” section as a fully integrated set, combining Who, How Many, What Impact, and Change to Existing Programs, so it’s ready for the application. Do you want me to do that?