WJA

Wladimir J. Alonso

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This piece exposes an unsolved scientific mystery regarding the capacity of certain organisms, like mosquitoes, to experience high levels of pain. Is the brain of a mosquito endowed with the complexity required to perceive strong affective states? This question is not only crucial for the calculation of moral weights but is also a fascinating topic in itself.

 

The Cumulative Pain analyses assume that the range of pain intensities varies from No-Pain to Excruciating in any sentient species. This range is needed in the method to make it flexible and adaptable across diverse taxa. Nevertheless, I personally believe that the range of different intensities of affective experiences evolved to match increasing levels of behavioral options, which are only possible with greater cognitive complexity. A mosquito, with an ephemeral lifespan and very limited behavioral choices, would not have been shaped by natural selection to require a wide range of affective intensities.

 

If this is the case, the hedonic capacity would differ far more between mosquitoes and humans than between humans and other cognitively complex animals that need to make decisions over a much more nuanced range of choices—indexed with several levels of affective intensity. Mosquitoes, therefore, might not experience more than the lowest intensity levels of suffering—something that, if true, would actually be excellent news.

 

Anyhow, this is a challenge that science needs to address with urgency.

Thank you very much Robert for all the links and sources—I really appreciate it. It’s great to hear that our work on animal suffering is being considered within your quantification efforts at the Organisation for the Prevention of Intense Suffering and the World Center for the Control of Excessive Suffering.

Regarding the definition of pain, we have actually proposed one, and it has been operationally useful. We designed it to be as universal as possible while explicitly addressing the need for special attention to higher manifestations of pain:

Pain is a conscious experience, evolved to elicit corrective behavior in response to actual or imminent damage to an organism’s survival and/or reproduction. Still, some manifestations, such as neuropathic pain, can be maladaptive. It is affectively and cognitively processed as an adverse and dynamic sensation that can vary in intensity, duration, texture, spatial specificity, and anatomical location. Pain is characterized as ‘physical’ when primarily triggered by pain receptors and as ‘psychological’ when triggered by memory and primary emotional systems. Depending on its intensity and duration, pain can override other adaptive instincts and motivational drives and lead to severe suffering”

 


 

Great idea! Our efforts with the Welfare Footprint Project aim to provide the most rigorous and comprehensive information possible to support initiatives like this—whether in the form of an app, website, or large language model. By developing tools to systematically quantify and map suffering across species and contexts—and already conducting various analyses in this direction—we hope to contribute the necessary data and frameworks to ensure such a tool is both scientifically robust and impactful.

Hi Robert,

I really appreciate your kind words. I’d be happy to discuss the topics you’re interested in—whether in a web meeting or through ongoing message exchanges here, whichever you prefer.

Your idea of an app addressing all suffering-related questions is excellent. We hope that the results from the Pain Atlas Project can serve as a valuable source of information for such an initiative. We continue working on this project—let’s see where it leads us.

I wish you great success in the idea you are putting forward to create this center and commend the inspiring vision behind it. It is clear from your biography (which I just learned from the link to your bio) that you have dedicated your life to reducing suffering—something truly remarkable.

I would just like to highlight a key point regarding the assertion that there is a lack of “standardized metrics for measuring and comparing different types of suffering.” I believe you will be glad to know that the Welfare Footprint Framework provides a universal methodology for quantifying affective states, including both pain and pleasure, in a biologically meaningful way. Specifically for suffering, this framework incorporates the Cumulative Pain Metric, which is expressed in units of time spent in varying intensities of negative affective states. This metric allows for direct comparison of different sources of suffering across conditions and interventions.

The notation tool of the Pain-Track enables detailed analysis of the temporal dynamics of suffering, grounded in evidence from diverse fields such as physiology, neurology, pharmacology, behavioral science, and evolutionary biology.

These standardized tools and metrics not only make suffering more measurable but also facilitate informed decision-making and comparisons across a wide range of contexts. For example, the Welfare Footprint Framework has been applied to quantify welfare impacts in animal production systems, guiding policy decisions and reforms. For more details, please visit www.welfarefootprint.org.

Hi Sam,

It’s clear we agree on the most important point: that promoting transparency is an urgent and essential step toward achieving significant welfare gains.

Where we differ is in strategy. While you believe that demanding technological-blocking legal restrictions on AI technology will create the necessary pressure to achieve transparency, I believe that directly advocating for something widely supported in society—namely transparency in food production—will better align us with a greater portion of society, increasing the chances of achieving this transparency much sooner.

I would focus our energy not only on demanding transparency but also on ensuring that it is thorough. There are many potential alignments with public health, environmental, and workers’ rights advocates, who could become powerful allies in this effort.

Hi Sam,
Thank you for sharing your thoughts. You mentioned that AI is already contributing to worsening conditions, but I’m not fully convinced that the examples you provided support this claim. Both examples seem to reflect broader trends of technological intensification, rather than generative AI specifically (which wasn’t available at the time those developments occurred). My focus is on generative AI, while other forms like machine learning and deep learning are already deeply embedded in industry practices.

That said, my main point remains: other things being equal, and acknowledging that factory farms are, unfortunately, a current reality, I hold an optimistic view of AI’s introduction into the industry: AI can monitor and address key production factors that overlap with welfare concerns, such as body scores, heat stress at the individual level, and the detection of injuries or diseases, far more effectively than traditional methods.

Rather than advocating for the abolition of AI in factory farming, I believe we should focus on campaigning for transparency. Specifically, the data gathered by AI and other monitoring technologies should be made accessible to independent stakeholders. This would create greater accountability and improve oversight.

Transparency-focused legislation is more plausible than bans on AI across an entire sector. It’s difficult to argue against the idea that the food industry should be transparent about its non-proprietary practices, particularly when animal welfare is concerned. While I’m not naive about existing challenges, such as ag-gag laws and potential loopholes, the chances of passing transparency laws are higher than prohibiting the use of technology outright.

Hi,

Thanks for the comments and the links.

I had the opportunity to share a panel with Sam, and I really like his work. That said, it’s true that we have differing perspectives on the role of AI for farmed animals. As described in this article, we don’t believe the impact of AI will be net negative—in fact, it could have positive aspects in certain areas.

For instance, we are aware of a company using AI to detect signs of mistreatment or illness by analyzing images of carcasses at processing plants. With this information, they plan to address issues with suppliers whose animals exhibit these problems. Such an approach should create significant incentives to tackle welfare abuses or neglect. While the overall conditions for animals may remain far from ideal, these improvements could represent meaningful progress in certain aspects of their welfare.

As for suggestions to ban or limit the use of AI in these systems, while I understand the reasoning behind them, I believe such measures are logistically and politically unfeasible. It would be akin to attempting to ban computers or the internet in the animal production sector when those technologies first emerged.

Indeed, like any technology, we must be vigilant about potential negative consequences. For example, back in the day, we were among those who signed against experiments involving the creation of potential pandemic pathogens—a stance that history has since validated, as we now know all too well. However, I do not view large language models (LLMs) in the same light. I believe LLMs will inevitably become a primary source of information for society, and this can be a very positive development. One way to guide this technology toward beneficial outcomes is by feeding it original scientific sources that have already been published.

Regarding the impact of AI on animal welfare, this is, of course, a critically important topic. We wrote a piece on our position on this some time ago but hadn’t published it until now. Motivated by your comment, we plan to do so in the coming days, and I would appreciate your thoughts on it once it’s available.