A Five-Year Plan to Ensure AGI Benefits All Animals
The most recent and comprehensive survey of AI experts predicts a 10% chance of AI surpassing human abilities by 2027 and a 50% chance by 2047. Some of the world’s leading AI figures—including the CEOs of NVIDIA, DeepMind, OpenAI, and Anthropic—believe artificial general intelligence (AGI) could arrive in less than five years.
Whatever the timeline may be, the next five years are undeniably our best chance to influence how AGI will impact animals. Current AI systems already outperform humans in persuasion by up to 81.7% and exhibit concerning speciesist biases that actively harm animals.
Companies exploiting animals have embraced this persuasive power of AI with open arms. Wayne-Sanderson Farms employs an AI chatbot, and KFC saw a 15% revenue boost through AI-driven marketing. Industry events like the Chicken Marketing Summit promote AI as a tool to maximize profits, further entrenching its role in accelerating demand for animal products.
AI is also reshaping factory farms and slaughterhouses themselves. The $3 billion precision livestock farming (PLF) industry is projected to exceed $8 billion by 2033, accelerating the industrialisation of animal agriculture. Whilst occasionally marketed as welfare-improving, research proves that PLF introduces at least 12 significant harms to animal welfare, including invasive devices, technical failures, inaccurate predictions, inadequate welfare monitoring, reduced animal care, increased industrialisation of farms and greater consumption of animal products.
The Default Path: Automated Exploitation at an Unprecedented Scale
Without intervention, AI will turn factory farming into a system of unimaginable cruelty and automated exploitation. AI systems will manage feed, lighting, temperature, and animal movement with ruthless precision to maximize productivity at the expense of animal welfare. Automated slaughterhouses will increase killing speeds while reducing oversight to pure production metrics and factory farms will operate with minimal human involvement, driving costs down and suffering up.
Advanced AI systems will continue to be deployed by marketing departments to shape public perception, counter animal advocacy efforts, and reinforce industrial animal agriculture's dominance of our food system. This will make meaningful reform virtually impossible as AI-powered industry messaging drowns out advocacy efforts, systematically undermines animal advocates and accelerates the global expansion of intensive farming.
This isn't a distant dystopia - it's already beginning. 26-story skyscraper farms in China leverage AI to slaughter more than a million pigs annually, AI-powered automated battery farms house more than 2 million chickens each, shrimp farms in the U.S. use AI to kill more than 2 million shrimp per year and autonomously controlled shipping containers in the UK breed black soldier flies to be fed to factory farmed chickens, intensifying the suffering of both species. These systems already exist and are expanding rapidly.
The Alternative: A Five-Year Plan to Harness AI for Animal Liberation
While the rise of AI in factory farming is already underway, we still have time to change course. The same AI capabilities that could entrench exploitation could instead become powerful tools for animal protection.
To seize this opportunity, we need a clear framework for evaluating and prioritising potential interventions. The following analysis provides a comprehensive evaluation of the most promising available interventions, scored and tiered to guide strategic deployment of movement resources.
Strategic Framework and Intervention Analysis
To systematically evaluate each intervention's potential, we developed a comprehensive scoring methodology that considers both immediate impact and long-term strategic value. Each intervention is scored on a 0-10 scale based on five equally weighted factors:
Impact: 2 points for major effects, 1 for moderate/regional effects, 0 for limited effects
Tractability: 2 points for clear implementation path, 1 for moderate challenges, 0 for significant obstacles
Neglectedness: 2 points for no current work, 1 for some work with gaps, 0 for well-covered areas
Resources Required: 2 points for minimal resources, 1 for moderate resources, 0 for intensive resources
Risk Level: 2 points for minimal risks, 1 for manageable risks, 0 for significant risks
We evaluated interventions using three frontier AI models - Gemini 2.0, Claude 3.5 Sonnet, and ChatGPT o1 - to generate objective scores. While several interventions developed by our team received high rankings in this evaluation, we took multiple steps to minimise potential biases in our assessment process.
To ensure fair comparisons, we had Claude 3.5 revise all intervention descriptions to maximize neutrality before submitting them for scoring. The models then independently evaluated each option in isolation, without any memory or prior context from previous evaluations. Final rankings were determined by averaging their scores. The complete prompt and model responses are available in the appendix.
We recognise that different experts may reach different conclusions based on their unique frameworks and priorities. We encourage others to adapt and apply this evaluation methodology to conduct their own independent assessments.
The following sections present the comprehensive rankings and detailed analysis of each intervention, ordered by their aggregate AI evaluation scores.
Ranked List of Interventions
1. Create Unified Animal Advocacy Database (Score: 8.33)
Develop a comprehensive, open-source database that aggregates information from animal advocacy organizations, serving as training data for AI models and enabling retrieval-augmented generation (RAG). APIs will allow organizations to securely share and access data.
Immediate Benefit for Animals: Provides actionable insights for smarter interventions and supports AI-driven tools and agents.
Future Link to AGI Alignment: Provides AGI with high-quality, animal-centric training data.
Current Work: Open Paws has signed data sharing agreements with 30+ organisations and is planning to release this database in early 2025.
Current Gaps: Requires more organizations to share data. Efforts needed to ensure data utilisation by advocacy organizations and AI labs.
2. Develop Animal Impact Assessment Standards (Score: 8)
Create frameworks and benchmarks to evaluate AI systems' direct and indirect effects on animals, with metrics for companies and regulators to assess welfare implications.
Immediate Benefit for Animals: Ensures AI technologies minimise harm to animals and influence industry practices toward ethical standards.
Future Link to AGI Alignment: Establishes animal welfare considerations into AGI decision-making.
Current Work: Hive is in the process of developing a benchmark and Open Paws is training AI models to rank outputs from AI systems based on their impact on animals.
Current Gaps: Needs wider adoption of benchmarks and models.
3. Build Campaign Success Prediction Systems (Score: 8)
Use machine learning to analyse historical campaign data and predict effective advocacy approaches, including timing, messaging, and intervention types.
Immediate Benefit for Animals: Optimises resource use and maximises campaign impact, leading to better outcomes for animal welfare.
Future Link to AGI Alignment: Aligns AGI with effectively achieving positive outcomes for animals.
Current Work: Open Paws is releasing campaign success prediction models in early 2025.
Current Gaps: More data-sharing partnerships needed.
4. Develop Specialized AI Models for Animal Advocacy (Score: 7.67)
Fine-tune AI models for animal advocacy tasks, such as content creation. These models will amplify messaging and reduce workload. They can also be used in advanced automations and agentic systems to take autonomous action on behalf of animal advocates.
Immediate Benefit for Animals: Scales animal advocacy efforts and reduces workload.
Future Link to AGI Alignment: Supports the development of open-source datasets, models, and research that prioritise animal welfare, creating valuable resources for ethical AI training.
Current Work: Open Paws is releasing specialised AI models in early 2025.
Current Gaps: Adoption of these resources by both AI labs and animal advocacy organizations remains a key challenge.
5. Provide AI Training for Advocates (Score: 7.67)
Develop programs teaching advocates to effectively use AI tools, from basic literacy to advanced applications.
Immediate Benefit for Animals: Increases the effectiveness and efficiency of animal advocates.
Future Link to AGI Alignment: By equipping animal advocates with AI skills, they may be able to contribute to the AI industry directly, potentially expanding their influence within key decision-making processes.
Current Work: Electric Sheep provides an online course for AI in Animal Protection and NFPs.ai provides trainings, webinars, consultations and workshops for animal charities.
Current Gaps: Despite the availability of some training programs, there is a lack of widespread, accessible, and affordable AI education tailored specifically to animal advocacy organizations and individuals. Many advocates may be unaware of how AI tools can benefit their work or lack the resources to integrate these tools effectively. Additionally, current programs may not cover more advanced applications.
6. Support Alternative Protein AI Development (Score: 7.33)
Facilitate AI research for alternative proteins through shared datasets, open-source tools, and collaboration with companies in the field. While this area has seen significant investment, a key limitation remains in data sharing due to competitive concerns.
Immediate Benefit for Animals: Optimises the production of alternative proteins, making them tastier, cheaper, healthier and more sustainable.
Future Link to AGI Alignment: Access to comprehensive, open data sets would allow AGI to optimize solutions for alternative protein development far faster and more effectively than fragmented or proprietary data would permit.
Current Work: Companies like NotCo are addressing data-sharing challenges by offering AI-as-a-service models, incentivising companies to share data to improve performance. Nonprofits such as Food Systems Innovation and the Good Food Institute have also made strides in advancing AI in alternative proteins.
Current Gaps: The primary challenge is fostering greater collaboration and data sharing among alternative protein companies. Adoption of shared AI frameworks by industry players is critical to unlocking further progress.
7. Integrate AI Tools into Advocacy Organizations (Score: 7.33)
Provide technical assistance to help organizations adopt AI tools for tasks like social media management, donor outreach, and campaign analytics.
Immediate Benefit for Animals: Increases efficiency, enabling more resources for direct advocacy.
Future Link to AGI Alignment: As animal advocacy organizations become bigger customers of AI systems, they gain leverage as stakeholders, prompting AI companies to consider their needs and perspectives. This creates an avenue for advocates to shape the development of AI tools in ways that align with compassionate and ethical priorities, ultimately influencing AGI systems to reflect these values.
Current Work: Some organizations are adopting AI tools, but adoption remains sporadic and uncoordinated.
Current Gaps: Needs structured playbooks, consultants, and funding to standardize and scale adoption across organizations globally.
8. Implement Animal-Friendly Data Labelling Standards (Score: 7)
Campaign for data labelling companies (who are hired to provide training data to AI companies) to integrate animal welfare guidelines into annotation processes.
Immediate Benefit for Animals: Ensures AI systems are trained on ethical data, preventing harmful biases in decision-making.
Future Link to AGI Alignment: Embeds ethical considerations into foundational datasets, shaping AGI values from the outset by providing high-quality data that includes animal welfare considerations.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Advocacy campaigns to encourage the adoption of animal welfare guidelines by labelling companies are lacking. Additionally, establishing best practices and ensuring widespread adoption by both industry players and advocacy organizations remains a significant challenge.
9. Present at AI Ethics Conferences (Score: 7)
Promote animal welfare in AI ethics discussions by organizing workshops and submitting papers at key conferences.
Immediate Benefit for Animals: Positions animal welfare as a central issue in AI development, ensuring it becomes part of the broader ethical discourse.
Future Link to AGI Alignment: Ensures animal welfare is prioritized in AGI alignment by influencing thought leaders and policymakers in AI ethics.
Current Work: Some papers on the intersection of AI and animal ethics have been submitted.
Current Gaps: Needs dedicated academic collaborations, increased funding, and stronger representation in high-profile conferences to amplify impact.
10. Advocate for Animal Protection in AI Laws (Score: 7)
Push for legal requirements to consider animal welfare in AI development and deployment.
Immediate Benefit for Animals: Prevents harm from AI-driven technologies and establishes protective standards that safeguard animal welfare.
Future Link to AGI Alignment: Creates a legal framework that integrates ethical considerations into AI governance, ensuring sentient beings are protected.
Current Work: Organisations like Anima International and Open Paws are currently participating in working groups drafting the codes of practise for the EU AI Act.
Current Gaps: Requires more legal expertise, lobbying support, and collaboration with advocacy groups to build momentum for legislative change.
11. Deploy Social Media Content Automation Systems (Score: 7)
Develop and deploy AI systems to optimize social media content distribution for advocacy, identifying viral potential and scheduling posts effectively.
Immediate Benefit for Animals: Amplifies advocacy reach and engagement, driving public awareness and support for animal welfare.
Future Link to AGI Alignment: By optimising social media content to amplify animal-friendly narratives, these systems contribute to shaping social media algorithms toward compassionate content. Given that many social media companies are actively advancing towards AGI, ensuring their platforms prioritize humane and ethical content can influence AGI training data to align with animal welfare values.
Current Work: Open Paws has developed and tested early versions of these tools.
Current Gaps: Requires additional resources, better scalability, and training for advocates to fully leverage these tools.
12. Reform Search Engine & Social Media Content Policies (Score: 6.67)
Engage with tech companies to improve policies for fair representation of animal welfare content and reduce misinformation.
Immediate Benefit for Animals: Increases visibility of accurate, compassionate advocacy content while countering harmful narratives.
Future Link to AGI Alignment: By influencing search engine and social media algorithms to prioritize humane and ethical content, we contribute to shaping the training data for AGI. These platforms, actively working towards AGI, could integrate more aligned training datasets if humane content becomes more prevalent.
Current Work: Limited engagement with platforms, with some exploratory advocacy efforts in place.
Current Gaps: Requires stronger coalitions to effectively engage with major platforms and dedicated lobbying to push for algorithmic transparency and ethical prioritisation.
13. Partner with AI Ethics Organizations (Score: 6.67)
Collaborate with AI ethics organizations to prioritize animal welfare in broader ethical AI frameworks.
Immediate Benefit for Animals: Ensures animal welfare becomes a key consideration in ethical AI standards, influencing how AI impacts animals across industries.
Future Link to AGI Alignment: Builds alliances to ensure AGI systems respect all sentient beings and align with ethical frameworks that prioritise animal welfare.
Current Work: Minimal work has been done in this space beyond informal conversations.
Current Gaps: Needs more formalised collaborations, funding for joint initiatives, and stronger integration into prominent ethical AI networks.
14. Pressure Cloud Providers to Restrict PLF (Score: 6.33)
Advocate for major cloud providers to stop serving Precision Livestock Farming (PLF) clients by highlighting ethical and environmental conflicts.
Immediate Benefit for Animals: Reduces infrastructure support for harmful factory farming technologies, limiting their scalability.
Future Link to AGI Alignment: By reducing reliance on these companies, their influence over AGI development is diminished, reducing the risk of prioritising profit-driven over ethical AI advancements.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Needs large coalitions and sustained pressure on major cloud providers to adopt restrictions.
15. Organize Tech Company Employee Networks (Score: 6.33)
Support employee groups within tech companies advocating for animal-friendly policies and practices.
Immediate Benefit for Animals: Leverages insider influence to advocate for systemic changes that align tech company operations with animal welfare goals.
Future Link to AGI Alignment: Promotes ethical awareness and decision-making within companies that are shaping the future of AI, embedding animal welfare considerations into their work.
Current Work: Early-stage initiatives are underway, with a few employees advocating for ethical policies internally. Largely informal networking.
Current Gaps: Needs structured support, funding for organising efforts, and tools to empower employees to advocate effectively within their companies.
16. Build Climate-Animal Advocacy Coalition Against PLF (Score: 6.33)
Form coalitions between animal and climate advocacy groups to oppose harmful AI-driven factory farming technologies.
Immediate Benefit for Animals: Strengthens advocacy efforts by leveraging the climate movement’s broader reach and resources.
Future Link to AGI Alignment: By building coalitions between animal and climate advocacy, these groups can exert greater combined influence on companies developing AGI.
Current Work: Limited collaboration between climate and animal advocacy groups, none that we are aware of specifically addressing AI.
Current Gaps: Needs dedicated joint projects, stronger outreach to climate groups, and frameworks to align goals effectively.
17. Launch Computer Science Student Education Programs (Score: 6.33)
Develop educational programs for computer science students focused on ethical AI and animal welfare.
Immediate Benefit for Animals: Inspires the next generation of AI professionals to integrate animal welfare into their projects and decision-making processes.
Future Link to AGI Alignment: Builds a pipeline of developers who are ethically informed and can influence AGI development to respect all sentient beings.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Requires funding to scale programs, partnerships with educational institutions, and the development of comprehensive curricula tailored to these goals.
18. Discourage Investment in PLF Technology (Score: 6)
Create resources to show ethical and business risks of investing in PLF technologies and promote alternatives.
Immediate Benefit for Animals: Reduces financial support for harmful industries, redirecting investments toward ethical technologies.
Future Link to AGI Alignment: Redirecting investment away from exploitative industries not only reduces their technological advancement but also diminishes their influence on shaping AI and AGI priorities.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Needs large investor networks and partnerships with financial analysts to create compelling resources and campaigns.
19. Establish Meat Industry Worker Union Alliances (Score: 6)
Build alliances with unions to address shared concerns about AI-driven automation in slaughterhouses and farms.
Immediate Benefit for Animals: Leverages shared interests to reduce automation that exacerbates animal suffering while protecting workers.
Future Link to AGI Alignment: By building coalitions between animal advocates and labor unions, these groups can exert greater combined influence on companies developing AGI.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Requires stronger partnerships, dedicated advocacy materials, and mutual education on shared goals.
20. Support PLF Developer Career Transitions (Score: 6)
Encourage developers to leave harmful PLF roles by providing career transition resources and support.
Immediate Benefit for Animals: Reduces the technical expertise available to harmful industries while fostering growth in ethical AI applications.
Future Link to AGI Alignment: Transitioning developers from exploitative industries to ethical sectors ensures the AI and AGI workforce prioritizes humane applications. These developers bring expertise that can guide AGI systems toward ethical optimisation processes, embedding values of compassion at their core.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Requires funding for retraining programs, career placement services, and active engagement with affected developers.
21. Recruit Animal-Friendly Search & Social Content Moderators (Score: 5.67)
Develop a network of moderators prioritising animal welfare, working to ensure ethical content curation on major platforms.
Immediate Benefit for Animals: Ensures fair representation of advocacy content while reducing harmful narratives.
Future Link to AGI Alignment: Embedding animal-friendly content moderation into major platforms influences the datasets and algorithms that will train AGI systems. By ensuring these systems are exposed to humane and accurate narratives, we increase the likelihood that AGI will internalise and prioritise ethical and compassionate values.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Needs funding to scale recruitment and training, along with stronger partnerships with major platforms to embed moderators effectively.
22. Lobby for Factory Farming AI Restrictions (Score: 5.33)
Advocate for legislation limiting harmful AI uses in factory farming, such as high-speed slaughter or welfare-reducing algorithms.
Immediate Benefit for Animals: Ends some of the worst harms caused by AI in factory farming, such as welfare-reducing algorithms and high-speed slaughter systems, reducing suffering and preventing further technological entrenchment of exploitative practices.
Future Link to AGI Alignment: Setting legal restrictions on harmful AI applications creates a direct precedent for embedding ethical considerations into AGI governance. By regulating how AI impacts sentient beings in factory farming, we send a clear message about the moral boundaries that AGI must respect, increasing the likelihood that AGI prioritises welfare for all sentient beings.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Requires expanded lobbying efforts, strategic alliances with policymakers, and broader coalitions to increase advocacy effectiveness.
23. Campaign for Food Industry Fact-Checking Labels on Social Media (Score: 5.33)
Advocate for labelling misinformation from food industries on social media, partnering with health and environmental groups.
Immediate Benefit for Animals: Challenges deceptive practices, promoting transparency and ethical consumption.
Future Link to AGI Alignment: Embedding fact-checking systems on social platforms can influence the training data for AGI, ensuring future AI systems prioritise accuracy and ethical standards in their decision-making.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Needs broader coalitions of advocacy groups and more consistent engagement with tech companies to advance implementation.
24. Coordinate Tech Company Shareholder Activism Campaigns (Score: 5.33)
Influence AI policies through shareholder activism, building coalitions of ethical investors.
Immediate Benefit for Animals: Encourages corporate accountability, pushing tech companies to integrate animal welfare considerations into their AI policies.
Future Link to AGI Alignment: By influencing corporate financial priorities through shareholder activism, this approach creates direct economic incentives for companies to prioritize ethical AI development.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Needs more coordination, including the establishment of networks of ethical investors and better tools for engaging shareholders.
25. Restrict AI Hardware Sales to PLF (Score: 4.67)
Pressure hardware manufacturers to restrict sales to PLF companies and create ethical use guidelines.
Immediate Benefit for Animals: Limits the technological infrastructure available for harmful farming automation, directly reducing animal suffering associated with PLF.
Future Link to AGI Alignment: Drives cultural shifts within the technology industry which influence long-term development priorities.
Current Work: We’re not aware of any organisations currently working on such campaigns.
Current Gaps: Requires structured advocacy initiatives, greater public awareness, and stronger coalitions to pressure manufacturers effectively.
Our Recommendations
A Movement-Wide Strategic Plan for AI in Animal Advocacy (2025-2029)
This strategic plan presents recommendations for how the animal advocacy movement could work together to ensure artificial intelligence benefits animals over the next five years. While we've structured these recommendations into phases for clarity, many of these interventions could be pursued immediately if resources and opportunities arise. Organisations should adapt these suggestions based on their capabilities, priorities, and circumstances.
Success will require coordinated effort across many organisations, with different groups taking leadership roles based on their expertise and capacity. The phases outlined here are designed to build upon each other, but they're flexible - early success in one area might accelerate opportunities in others, and some organisations may be ready to tackle "later phase" projects immediately.
Phase 1: Foundation Building (2025)
High-Impact Data & Tools Development
Establish movement-wide collaborations to contribute to and benefit from the unified animal advocacy database, specialised AI models, campaign success prediction models and outreach automation tools that Open Paws will release in early 2025
Coordinate between technical teams at different organizations to avoid duplicate efforts
Movement-Wide Capability Building
Develop step-by-step standardised playbooks for integrating AI tools into advocacy organizations
Expand the provision of AI workshops, trainings, consultations and tutorials to animal advocates
Alternative Protein Support
Create shared datasets and tools for alternative protein development
Set up data-sharing frameworks that protect IP while enabling collaboration
Phase 2: Education & Coalition Building (2025-2026)
Education Program Development
Launch computer science student education programs focused on ethical AI and animal welfare
Create curriculum materials for universities and bootcamps
Tech Worker Organisation
Create networks of tech workers at major AI companies
Establish employee resource groups focused on animal welfare
Develop advocacy materials for internal use by tech workers
Set up support systems for employee-led initiatives
Coalition Development
Form partnerships with established AI ethics organizations
Build climate-animal advocacy coalition specifically focused on PLF
Establish relationships with meat industry worker unions
Conference & Academic Presence
Submit and present papers at major AI ethics conferences
Establish research collaborations with academic institutions
Phase 3: Policy Engagement (2026-2027)
Policy Development
Advocate for animal protection requirements in AI laws
Participate in regulatory working groups
Build relationships with policymakers and regulators
Content Policy Reform
Engage with search engines and social media platforms on animal welfare content policies
Develop fact-checking and misinformation labelling systems for food industry claims on social media
Build network of animal-friendly content moderators
Phase 4: PLF Industry Pressure (2027-2028)
Cloud Provider Campaigns
Launch campaigns targeting major cloud providers serving PLF clients
Hardware Restrictions
Pressure hardware manufacturers to restrict PLF sales
Build industry support for sales restrictions
Developer Transition Support
Create career transition resources for PLF developers
Establish job placement services in ethical sectors
Phase 5: Financial & Corporate Pressure (2028-2029)
Investment Pressure
Launch campaigns to discourage PLF technology investment
Shareholder Activism
Coordinate tech company shareholder activism campaigns
Build support among institutional investors
Corporate Policy Reform
Advocate for animal welfare in AI development policies
Build corporate accountability frameworks
Conclusion
The next five years represent a critical window to shape the future of AI’s impact on animals. By prioritising high-impact interventions and strategically leveraging AI’s capabilities, we can counter the rising threats of automated exploitation and ensure that emerging technologies are harnessed for animal protection. This is might be our one and only chance to redefine the trajectory of AI toward a more compassionate and just future for all sentient beings.
Appendix
Prompt (used to generate rankings for interventions)
Generate a single table to rank each of these potential interventions by the following factors and add those rankings together to generate a total score from 0 to 10, beginning with the highest ranking interventions, then listing the rest in descending order:].
List of factors to rank:
Impact (0-2):
2: Could significantly affect animal welfare
1: Moderate effects on specific areas or regions
0: Limited or uncertain effects
Tractability (0-2):
2: Clear path to implementation with existing tools/approaches
1: Moderate challenges, but feasible
0: Significant obstacles or resistance likely
Neglectedness (0-2):
2: No organizations currently working on this
1: Some work being done, but significant gaps
0: Well-covered by existing efforts
Resources Required (Inverse 0-2):
2: Minimal resources needed
1: Moderate resource requirements
0: Intensive resource requirements
Risk Level (Inverse 0-2):
2: Minimal downside risks
1: Moderate but manageable risks
0: Significant risks or potential backfire effects
List of potential interventions:
Advocate for Animal Protection in AI Laws: A policy initiative to establish requirements for considering animal welfare impacts in AI development regulations. This works within existing AI regulatory frameworks and policy infrastructure, with most work focusing on policy advocacy and drafting.
Build Campaign Success Prediction Systems: A machine learning system analyzing historical campaign data to forecast advocacy effectiveness. The system uses existing collected campaign data and volunteer technical expertise, with cloud computing resources already secured through credits.
Build Climate-Animal Advocacy Coalition Against PLF: An initiative to create coalitions between animal advocacy and climate organizations regarding AI in agriculture. This involves coordinating between existing environmental and animal advocacy groups, focusing primarily on relationship building and strategic alignment.
Campaign for Food Industry Fact-Checking Labels: An initiative to implement labeling systems for food industry claims on social media platforms. This works within existing platform fact-checking systems but requires sustained engagement with platform policies and content moderation teams.
Coordinate Tech Company Shareholder Activism: An effort to influence tech companies' AI policies through shareholder resolutions and investor engagement. This operates through established corporate governance mechanisms and existing ESG (Environmental, Social, Governance) frameworks.
Create Unified Animal Advocacy Database: A database system aggregating information from animal advocacy organizations with APIs for data sharing. The project has established data sharing agreements with multiple organizations and utilizes existing cloud infrastructure.
Deploy Social Media Content Automation Systems: Development of systems to optimize content distribution across social media platforms. This uses existing AI and automation tools while working within platform guidelines and terms of service.
Develop Animal Impact Assessment Standards: Creation of frameworks for evaluating AI systems' effects on animals. This involves developing metrics and assessment tools that could be adopted by companies or regulators.
Develop Specialized AI Models for Animal Advocacy: Development of AI models trained on campaign and advocacy materials. This utilizes existing computing resources and collected datasets, with technical work performed by volunteer developers.
Discourage Investment in PLF Technology: An initiative to inform investors about various aspects of Precision Livestock Farming technology investment. This works through existing investment networks and financial analysis frameworks.
Establish Meat Industry Worker Union Alliances: An effort to build connections with labor unions around automation in meat processing facilities. This involves identifying shared interests and developing collaborative approaches to workplace automation issues.
Implement Animal-Friendly Data Labelling Standards: A project to develop guidelines for how animals are represented in AI training data. This works with existing data labeling companies and their current annotation processes.
Integrate AI Tools into Advocacy Organizations: A systematic approach to incorporating AI tools into advocacy operations. This involves adapting existing AI technologies for specific organizational needs and providing implementation support.
Launch Computer Science Student Education Programs: Educational initiatives about AI ethics for computer science and AI students. This works within existing university structures and technical education frameworks.
Lobby for Factory Farming AI Restrictions: A focused policy effort to establish specific limitations on AI applications in farming operations. This involves working with policymakers to draft and implement targeted regulations.
Organize Tech Company Employee Networks: Support for employee groups interested in AI ethics within technology companies. This works through existing employee resource group structures while respecting corporate policies.
Partner with AI Ethics Organizations: Development of partnerships between animal advocacy and AI ethics organizations. This involves finding areas of mutual interest within existing AI ethics frameworks and discussions.
Present at AI Ethics Conferences: Efforts to include animal welfare considerations in AI ethics academic discourse. This operates through established academic conference systems and publication venues.
Pressure Cloud Providers to Restrict PLF: An initiative to engage with cloud computing providers about their services to agricultural AI applications. This works through existing corporate responsibility frameworks and customer feedback channels.
Provide AI Training for Advocates: Development of training programs teaching advocates to use AI tools effectively. This builds on existing technical training frameworks and established AI tools.
Recruit Animal-Friendly Search & Social Content Moderators: An initiative to develop networks of content moderators familiar with animal welfare issues. This works within existing content moderation systems and quality rating frameworks.
Reform Search Engine & Social Media Content Policies: Engagement with platforms about their handling of animal welfare and agricultural content. This operates through established platform policy channels and content guidelines.
Restrict AI Hardware Sales to PLF: An initiative to engage with AI hardware manufacturers about their customer policies. This works through existing corporate sales channels and responsibility frameworks.
Support Alternative Protein AI Development: Support for AI research in alternative protein development through tools and datasets. This operates within existing food technology research and development frameworks.
Support PLF Developer Career Transitions: Programs to assist AI developers interested in changing roles or sectors. This works through existing professional development and job placement channels.
