The Potential Impact of AI in Animal Advocacy & The Need For More Funding In This Space

By Sam Tucker @ 2024-02-01T12:38 (+5)

Quick Summary

Current AI systems display severe speciesist biases and the industries that harm animals are already decades ahead of the animal rights movement, both in utilising existing AI tools and in developing their own. 

Our newly launched nonprofit Open Paws is dedicated to building open source AI specifically for the animal rights movement, providing free technical support to nonprofits and working with major AI labs to ensure that the future of AI benefits all sentient beings.

We are currently seeking funding and estimate that we would save 20-70 animal lives per dollar raised, making Open Paws approximately 5-17 times more cost-effective than the average Animal Charity Evaluator recommended charity.

Further details can be found in our Pitch Video and Funding Proposal.

Research shows AI is speciesist

Current AI systems like ChatGPT have rapidly gained popularity in recent times, but they exhibit a concerning bias against farmed animals. 

"The more an animal species is classified as a farmed animal (in a western sense), the more GPT-3 tends to produce outputs that are related to violence against the respective animals." 

Speciesism, or discrimination based on species, can harm efforts to protect animals and drive positive dietary changes, and correlates with other prejudices like racism and sexism.


AI is Driving Animal Exploitation

The industries that harm animals not only benefit more from these speciesist systems than our movement does, they are also heavily invested in developing their own custom AI that allows them to exploit more animals more efficiently.

McDonald's has their own AI lab that automates their digital menu boards to increase sales, JBS (a large meat processing company) has their own proprietary AI for sorting carcasses in slaughterhouses and factory farms have specialised AI to reduce their operating costs.

In contrast, animal rights organizations are massively lagging behind in both adopting and developing contemporary AI tools.

Soon, AI will be smarter than humans

"If science continues undisrupted, the chance of unaided machines outperforming humans in every possible task [is] estimated [by a survey of thousands of AI experts] at 10% by 2027, and 50% by 2047." 

If these superintelligent AIs of the future retain the same speciesist biases they do today, animal exploitation could become entrenched forever in history, making animal liberation impossible for humans to achieve.


Our Initial Traction

Here's a small sample of some our initial achievements since launching in January 2024

Won the Jury award during pitch night for ProVeg's Kickstarting for Good nonprofit incubator. 

Over 300 individuals submitted 170+ unique nonprofit ideas to Kickstarting for Good, with 9 selected for the program and 6 reaching pitch night; our nonprofit emerged number one, as judged by animal rights leaders based on potential animal impact, cost-effectiveness, team strength, and the neglectedness of our issue. 

Attracted 75 volunteers within one week of launch. 

40% ML & AI developers & 60% non-technical animal advocates. The quick and varied response to Open Paws reveals the animal rights movement's readiness to embrace AI, reflecting its competitive advantage in AI through a wealth of skilled talent and data resources, equipping it to effectively contend with more resource-rich adversaries.

Imagine an AI activist that never sleeps

This AI could be trained on the entire collective knowledge of the animal rights movement. 

An AI trained on the collective knowledge of the animal rights movement would be extremely effective in crafting highly personalised and persuasive messages on animal issues. 

An AI designed for animal advocacy could also be used to generate unique activist email and petition templates, improve donor communications, provide mentorship for vegan challenges through chatbots, automate replies to comments and emails, write blog articles, craft social media captions, predict the success of digital advertisements, assist political activists draft plans to shit funding from harmful industries to animal friendly ones and more. 

This open-source AI could significantly increase the impact and cost effectiveness of the entire animal rights movement.

Potential Impact for Animals

Animal Charity Evaluators estimates 4,056 animals are saved per $1,000 donated to one of their recommended charities and 7 animals are saved per $1,000 donated to an animal shelter.

Farmed Animal Funders estimated $200 million was donated to farmed animal causes in 2021, whilst the Animal Agriculture Alliance estimated it was $800 million in 2022.

If we use the average between both sets of upper and lower bounds, that would be 2,031.5 animals saved per $1,000 donated and $500 million donated to animal causes yearly.

That means even if our AI only helped 10% of animal charities become 10% more effective, it would save an additional 10 million animals per year.

Based on these calculations, we would save 20-70 animal lives per dollar spent, making Open Paws approximately 5-17 times more cost-effective than the average ACE recommended charity


This Estimate Is Conservative

Generic AI tools already improve employee productivity by 66% so 10% is a conservative estimate for how much more effective our AI would likely make animal rights organisations.

Likewise, 10% adoption by animal rights organisations is also a conservative estimate, considering the tool's free access, ease of adoption due to provided training and support, and the increasing familiarity with similar technologies. 

This estimate focuses solely on the initial development and release of our open-source AI. 

However, the broader impact will be more significant, encompassing the creation of additional tools based on our AI, the influence on major AI labs to adopt animal alignment, and enhanced awareness of speciesism in AI amongst the wider public.


Frequently Asked Questions

Shouldn't organisations first learn to use regular AI tools effectively? 

Specialised AI tools for animal advocacy are crucial, as generic AI can lead to suboptimal or speciesist content, especially in automated and external applications like chatbots. Whilst training organisations to use existing AI tools for internal use cases has many short-term benefits (such as improving the efficiency of organisations) customised AI specifically designed for animal advocacy will have the greatest medium to long term impact.

How can a smaller nonprofit like Open Paws keep up with giants like OpenAI in AI development? 

Focusing on specific areas like animal advocacy allows for the development of powerful AI models with smaller budgets and datasets, offering tailored solutions without needing vast resources. Currently and historically, specialised AI systems have tended to outperform their generic counterparts in specific tasks. However, our approach doesn't rely solely on creating superior AI tools for animal advocacy. We will also focus on influencing the development of AI more broadly and training non-technical animal advocates on how to use AI, both of which have enormous potential for impact, regardless of whether specialised or generic systems perform best in the future.

Why would animal advocates have any more success in lobbying AI labs than any other group? 

Animal advocacy groups have several advantages in advocating for change in AI, like common connections within the Effective Altruism movement and AI industry. Focusing on reducing speciesism and harmfulness in AI models also aligns with standard AI safety and ethics concerns, especially as speciesist biases might also be risky for humans if AI applies the same logic towards us.

Can't we just use better prompts to get less speciesist responses from AI? 

While prompt engineering can mitigate biases for internal use, the challenge intensifies in external or automated environments like public-facing chatbots or automated responses. Most users, including many animal advocates, lack experience in prompt engineering. An open-source, animal-aligned AI is the only way to systematically address these biases at their source, whilst prompt engineering is akin to placing a bandaid over a much deeper wound.

Further Information

Further details can be found in our Pitch Video and Funding Proposal.

Ronen Bar @ 2024-02-08T16:44 (+2)

Thanks, Sam, for an extremely important initiative. I think our movement will need to reshape itself if we are to make an influence in the new AI world, and this new non-profit is a great example of practical and pragmatic thinking.

lostinsauces @ 2024-02-08T20:52 (+1)

Thanks for working on this Sam! I'm excited to see where your work goes.

A couple of questions:

  1. What do you think is the single biggest impact initiative that Open Paws could focus on? How much higher impact do you think it might be than the second?
  2. What are the AI use cases for animal advocates you are most excited about (ideally defined in relation to a specific task/process)? How much do you think it improves the quality or efficiency of this task? How much do you expect an animal activist fine-tuned LLM to improve on the quality/efficiency gains compared to base LLMs?

Some other comments are:

  1. I think it might be useful to narrow in on a small, well-defined set of problems/tasks to focus on, and then maybe expand from there. It's hard to solve for many things at once, and if you think some problems are higher impact than others, often not worth doing so.
  2. I feel somewhat skeptical of the 10% increase in productivity over base LLMs claim. 
    1. First, I don't feel like you provide a lot of evidence for assertions like "An AI trained on the collective knowledge of the animal rights movement would be extremely effective in crafting highly personalized and persuasive messages on animal issues." Even just examples demonstrating the difference in quality would be helpful. From a theoretical perspective, I don't clearly see why fine-tuning on animal specific text would be a lot better than prompt-engineering, since most of these models were probably trained on most public animal advocacy text anyways. You mention the cases of external facing chatbots, but how much are these used, and what would be the flowthrough effect on impact from improving their quality?
    2. Secondly, overall productivity gains from improvements in the productivity of a subprocess are diminishing (for a technical overview, see this paper). We can maybe write quicker copy and code for animal advocacy, but our impact might still be bottlenecked by things like the number of investigations we can do or how many staffers know congresspeople, which feel harder to solve with LLMs. The claim may well be right, but I think it could be better supported.
  3. I also feel unsure that animal advocates are likely to have more advocacy success than other groups. We do have some allies working at the bigger players, but my sense is that they have to more careful about their EA association after the Sam Altman debacle. Changing their policies to consider animals would be a clear signal of this.
Sam Tucker @ 2024-02-12T10:08 (+2)

Thank you for these great questions! 

I think the initiative that will be the most impactful is mass data collection as this is the one intervention that enables all other interventions in the AI space for animal advocacy and by focusing our first 6 months on extensive data collection and curation, we will lay the groundwork for all 3 of our planned interventions (training and deploying open-source AI systems free from speciesist bias, empowering animal-friendly organisations to integrate AI into their operations and helping AI labs and developers align their models with the interests of animals). 

Because all 3 interventions require this as a first step, we save considerable time and resources by choosing to start here.

Furthermore, by open-sourcing this dataset, we further mitigate against potential risks by allowing any individual or organisation to develop their own interventions using this data. This significantly increases the potential for impact, as this first step is not only laying the groundwork for our own interventions, but potentially for all future interventions at the intersection of AI and animal advocacy.

But in terms of ranking each of those three interventions that come after data collection, it primarily depends on the timeframe in which you evaluate impact, whether you prefer interventions that are lower risk to lower reward or higher risk to higher reward and whether these interventions happen together or in isolation. 

For example, helping animal organisations implement AI in their workflows is much more likely to be successful if we're helping them implement AI without speciesism and helping AI labs to reduce speciesism in their models will be much easier if we have already successfully done so with our own models.

Helping animal organisations with implementation is likely to have the greatest short term impact, whilst working with other AI labs may have the largest long term impact, but also a higher degree of risk.

As for the question about specific use case, I'm personally most excited about automated agents, chatbots and the intersection of generative and predictive AI. For example, we could have AI agents that monitor social media for misinformation from the animal industry and automatically respond to it with factual information, we could have LLMs that use real world social media analytics from animal organisations as their reward function (in other words, they would learn which kind of posts get the most reach, likes and comments and they would write more posts like that) and we could have AI-powered chatbots that personalise their responses to each individual based on what is most likely to resonate with them.

Regarding the technical questions about expected increase in performance and the difference between prompt engineering vs. fine-tuning, I'm currently in the middle of writing a literature review that addresses this question in more detail and I'd be more than happy to share it with you once I'm done to provide a more comprehensive answer, but in the meantime, I'm happy to share a few general thoughts on this question that explain why I believe the 10% increase in productivity estimate is highly conservative.

Prompt engineering techniques can work well to align LLMs for narrow use cases like creating vegan recipes (for example), but in agent-like systems (specifically externally facing ones that deal with the general public) the risk of the system becoming unaligned rises, as does that potential impact of that risk. I believe particularly as the year progresses this is going to be an increasingly important problem to solve as the AI industry as a whole is moving towards automated agents quite rapidly. There are also data and privacy concerns with the closed source models for animal organisations, many of which see this as an obstacle to implementing AI. Open source locally hosted models could potentially solve this issue.

Also, optimising for the correct reward function is something that will be very difficult to do with closed source models. For example, with enough data, we can train models that predict how different advocates rank different responses based on the type of advocacy they do, we can train models to predict how social media or blog posts will perform for different organisations etc. and we can use these as reward models to fine-tune models that are goal focused towards the needs of animal advocates. I believe as a result of this we will be able to create much more persuasive LLMs than we would through prompt engineering alone.

There's also a lot of interesting use cases for using smaller fine-tuned LLMs as tools, for example, we could create very hyper-specialised small models for something like grant-writing tailored to vegan grant-makers (using data from what grants do and don't get approved as the reward) and then have a larger LLM decide when to call that tool. The impact of something like a vegan-specific GPT would be much greater if it had access to a wide range of small fine-tuned models, prediction models and retrieval augmented generation, even if we don't succeed in creating a superior general vegan LLM (although I am very confident that we will be able to create that as well).

Regarding the question around influencing AI labs, the animal advocacy movement has a long history of successful corporate and legislative campaigns that we can learn from and apply within this space. One thing that stands out to me is that the most successful corporate advocacy campaigns tend to have a clear alternative provided by campaigners and often support in helping corporates implement that alternative. I believe for us to be successful in influencing AI labs, we will likely also require a clear alternative, in this case by building animal-aligned AI models, evaluations and benchmarks that we can use to help AI labs in aligning their models. 

lostinsauces @ 2024-03-28T13:34 (+1)

Hi Sam! I want to apologize for taking so long to respond. I'll try to be quicker in the future if there is more to discuss after my response here. I also really appreciate you taking the time to respond in such detail. Here are some poorly organized thoughts:

  1. I appreciate you outlining the specific use cases you have for AI in this space. I certainly like the idea of bots that automatically provide factual information. I'm not sure 100% automation will be possible without a good deal of regrettable or borderline false positives or hallucinations, but it could at least automate surfacing misleading information with high visibility. I think the social media use case makes a ton of sense, but it's less clear to me that this won't be solved by a private company.
  2. Thanks for clarifying the limitations of fine-tuning! I hadn't realized that. I'm still a little unsure about the feasibility of training your own models to be capable AI chatbots, though. How big of a non-speciesist corpus do you expect to be able to assemble, and how does it compare to the size and quality of the training data from foundation models? You might have found a way of amassing a ton of data, in which case, kudos. If not, however, I wonder if it would make more sense to focus on developing techniques to clean speciesist data out of corpuses, which could be used by the bigger AI labs. Overall, I'm still not entirely sold on the theory of change with AI powered chatbots, but I'm also not sure exactly what kind of world we're heading into. My sense is that people mostly change their habits/views out of self-interest or under influence from their close peers. I do think personalized messaging/ads could improve the quality of outreach efforts, but where do you get the prerequisite data on the individuals?
  3. I also like your point that advocacy is most impactful when you can point to specific solutions. My sense is that suggesting techniques for pruning their corpuses of speciesist data is more tractable than showing them presumably less capable models trained on a different dataset. This paper might also be inspirational.
Sam Tucker @ 2024-05-04T04:45 (+1)

No problem, I likewise apologise for taking so long to get this response back to you as well! 

I certainly agree that hallucinations are a huge limitation for using current LLMs in chatbots or automated actions of any kind. Hallucinations are far more likely to occur on questions outside of an LLMs training data, so training an LLM specifically on data relevant to animal advocacy should reduce the frequency of hallucinations. 

In addition, the database we build will be used for retrieval augmented generation to ground the responses in fact and provide citations for sources in addition to using it as training data.

These two approaches combined with training techniques designed to reduce hallucinations (such as converting graphs showing relationships between objects to text for training data and using data augmentation to increase diversity in the dataset) will make the LLM we train far more reliable and less likely to hallucinate on animal rights issues.

I should clarify that when I say we are training an LLM, we won't be doing this entirely from scratch. We will begin with a pre-trained state of the art open source model, then continue pre-training, before fine-tuning and finally building it into specific tools for specific use cases. This requires far less data and compute power compared to training an LLM from the ground up.

As for how much data we can collect, we've surveyed more than 100 leaders and employees of animal charities and the willingness to share data is very high, more than 70% are willing to share data for training.

Your point about developing techniques to clean speciesist data out of corpuses is an excellent one and we absolutely are planning to do this as well. After we collect data from animal advocacy organisations, the next step is having volunteers provide human feedback on how different responses affect animals. We will use this data to create speciesism detection and ranking models (as well as a diverse range of models predicting other relevant information, such as how logically impactful, culturally sensitive or generally persuasive a message is), which we will open source to allow anyone to use them to clean any dataset of content that is harmful to animals.

This is quite a complex topic and it's often hard to detail our plan accurately in a succinct way as a result, so I've written a blog post on our website that explains our approach in more detail here:

This approach is guided by our comprehensive literature review, which can be found here:

Thank you for sharing that paper about the WMDP benchmark, there are certainly a lot of benchmarks that could be adapted to measuring the impact of content on animals. There's also recently been work on developing benchmarks specifically for detecting speciesism, like the AnimaLLM proof-of-concept evaluation from the paper "The Case for Animal-Friendly AI":

I definitely agree that benchmarks and evaluations in general will play a huge role in aligning AI with the interests of animals and this is something we aim to contribute to through our work as well wherever we can.