Welfare Footprint Institute https://welfarefootprint.org Quantifying Animal Welfare Thu, 05 Mar 2026 07:31:02 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://welfarefootprint.org/wp-content/uploads/2021/04/cropped-android-chrome-512x512-1-32x32.png Welfare Footprint Institute https://welfarefootprint.org 32 32 We Measure the Evidence. You Make the Call. Here’s Why. https://welfarefootprint.org/2026/03/04/we-measure-the-evidence-you-make-the-call-heres-why/ https://welfarefootprint.org/2026/03/04/we-measure-the-evidence-you-make-the-call-heres-why/#respond Wed, 04 Mar 2026 15:14:53 +0000 https://welfarefootprint.org/?p=11615
We Measure the Evidence. You Make the Call. Here’s Why. We often receive feedback from people who share a common goal: improving animal welfare as much as possible. When feedback … Continue reading We Measure the Evidence. You Make the Call. Here’s Why.
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We Measure the Evidence. You Make the Call. Here’s Why.

We often receive feedback from people who share a common goal: improving animal welfare as much as possible. When feedback is in the form of critique, it may come in two forms. One kind is scientific: about evidence, modeling choices, uncertainty, and gaps. That sits squarely within our scientific mandate, and we treat it as valuable input for revision. The other is normative: about what conclusions people should draw, or what actions people should take, given the evidence. That’s a legitimate debate, but it isn’t a scientific dispute, and it isn’t where an evidence-focused research institute is best placed to contribute.

This post is a standing clarification of principles: how we interpret criticism, what we do with it, and what we see as our role in the broader effort to improve animal welfare.

Two kinds of criticism, and why we treat them differently

In practice, critiques often get mixed together. Separating them makes the conversation more productive.

1. Methodological and evidentiary criticism

Our lane: building evidence-based, auditable welfare metrics

The Welfare Footprint Institute exists to do a specific kind of work: translate the best available scientific evidence about animals’ lived experiences into transparent, comparable welfare metrics, grounded in explicit assumptions, accompanied by uncertainty where appropriate, and open to revision as more evidence emerges. 

We don’t aim to deliver a “final answer” on welfare impacts. We aim to produce auditable, evidence-based metrics that remain explicitly conditional on their assumptions, and therefore improvable over time. This approach is aligned with Effective Altruism’s core principles: impartiality, careful reasoning under uncertainty, epistemic humility, and improving decisions to reduce suffering as effectively as possible.

Methodological critiques may include claims that a given module of the WF framework (e.g., the description of living circumstances) is not capturing reality well, or that a specific analysis used questionable assumptions, drew boundaries too narrowly, or misread the evidence on intensity, duration, or prevalence. This is exactly the kind of criticism we want. Where warranted, we address it the way science is supposed to work: by revising assumptions, expanding inventories, refining estimates, and/or widening uncertainty ranges. That is not a threat to the framework; it is precisely how the framework is meant to function. In that sense, we aim for what Nassim Nicholas Taleb calls “antifragility”: a system that doesn’t merely tolerate stress-testing, but improves because weaknesses can be exposed and corrected.

A note on scope, completeness and validity

Every assessment defines a specific scope of analysis depending on the aim. That means critiques may accurately point out that an analysis is not exhaustive. But “not exhaustive” does not mean “invalid.”  A proper claim of invalidity requires more than identifying what was left out — it requires showing that the omitted components (harms, life fates, life phases) lead to sufficiently prevalent, intense, and long-lasting experiences that can plausibly reverse the direction of claims or materially change the magnitude of the conclusion. Without that, the critique is an observation about scope, not a legitimate refutation of the result. We include an example of how this works in practice at the end of this post.

2. Valued based objections

A different type of feedback is concerned not with whether an estimate is accurate, but with how results might be interpreted socially or politically. Given the same welfare estimate, reasonable people can arrive at different ethical conclusions:

  • Some may focus on comparative harm reduction: “This option improves welfare substantially; it should be prioritized.”
  • Others may hold threshold-, rights-based, harm prevention views: “Any non-trivial amount of severe suffering is unacceptable; abstinence is the only acceptable choice.”
  • Still others may weigh trade-offs differently depending on uncertainty, moral risk tolerance, or strategic constraints: “Animal welfare needs to be viewed within the broader decision-making context”.

We understand why people can have these concerns: Those prioritizing harm prevention can worry that quantified and demonstrable improvements in animal production systems could be used to defend continued consumption, while others may worry that quantified harms will be used to justify stronger demands being placed on producers, objections to animal production practices or systems, or increased product costs. These concerns often have sincere ethical or strategic origins, but they are not, at root, a scientific dispute about the correctness of a welfare estimate. They are disagreements about ethical thresholds, strategy, and messaging. 

Like everyone else, the people involved in the Welfare Footprint Institute have personal values and moral intuitions, but as an Institute, we do not take an institutional position on which ethical framework is “correct.” Quantifying the welfare impact of a system is not a declaration that the system is morally acceptable. It is an attempt to describe, as accurately as possible, what the animals are likely experiencing. In fact, one reason to build time-based welfare metrics is that they can make important welfare improvements possible, by translating them into units that are visible, concrete, comparable, and auditable. 

As an Institute, we deliberately avoid presenting welfare measurement as a moral verdict. The central hope is simple: that better measurement helps make animals’ lives better by improving prioritization, revealing hidden hotspots of welfare loss, clarifying trade-offs, and making welfare impacts transparent and relatable to a wide set of decision-makers. Our role is to make the welfare-relevant evidence base clearer, so that individuals and organizations, whatever their ethical lens, can reason from a shared empirical foundation.

Example: cage and cage-free comparison: the need for quantification

In 2021, we analyzed the welfare impact of transitioning from cages to indoor cage-free aviaries. For each housing system, the analysis thus focused on the laying phase, and examined key welfare challenges likely to be affected by the transition. Challenges that are equally shared across systems, such as hatchery processes, would not change comparative results and overall conclusions and were therefore not included in the analysis. 

The comparison quantified cumulative time spent in negative affective states and incorporated major welfare harms known to differ across systems, including chronic behavioral deprivation in cages and higher prevalence of certain injuries in cage-free systems. The key finding was that cage-free aviaries were superior to cages in terms of hours of pain prevented even soon after a transition to cage-free environments. As with any welfare assessment, the scope was explicit and bounded: to estimate the welfare impact of the cage-free transition, not the impact of a whole life as an egg-laying hen in a system.  

Importantly, the assumptions and scope of this first analysis favored a more conservative estimate of the welfare benefits of cage-free housing. First, while we did not include harms associated with poor litter or air quality in poorly managed aviaries, we also did not consider several welfare costs of cages: increased levels of fearfulness, the absence of agency and control (conducive to learned helplessness), induced molting (still applied to caged layers in some countries but rarer in cage-free production), longer production cycles associated with end-of-lay deterioration, and the rearing phase, which if included would amplify estimated differences. Second, the analysis assumed that a given injury or disease was associated with the same duration and perceived intensity of pain regardless of housing system. Substantial evidence indicates otherwise, suggesting that similar welfare challenges produce more intense and longer-lasting pain in cages. Third, the prevalence data available for cage-free systems reflected facilities still in transition, before management experience had caught up with the decades of optimization behind caged production, so higher prevalence values of harms were assigned to cage-free systems. The welfare benefits of the cage-free transition are therefore likely to surpass the estimates from this first analysis. Ongoing work with a different goal — estimating the full welfare footprint of an egg — will extend these estimates to 100+ affective experiences across the full production cycle (breeding farms, hatcheries, production farms and abattoirs). 

One of the motivations for this work is the widespread assertion that no housing system is objectively better because each has its own welfare challenges. This assertion confuses the existence of trade-offs with their equivalence: the fact that each system has problems does not mean the impact of the problems are equal. Two systems can each have serious welfare challenges and still differ enormously in their overall animal welfare impact, because harms differ in how painful they are, how long they last, and how many animals they affect. Without quantifying these dimensions, any comparison remains an exercise in list-making. For example a chronic deprivation affecting every animal in a flock can be made to look equivalent to an injury affecting a small fraction of animals for a short time. The framework exists precisely to resolve this — by quantifying the intensity, duration, and prevalence of each harm so that comparisons rest on evidence rather than assertion.

]]> https://welfarefootprint.org/2026/03/04/we-measure-the-evidence-you-make-the-call-heres-why/feed/ 0 Welfare Footprint Institute The Scarcity of Trust in the Age of AI https://welfarefootprint.org/2026/02/04/the-scarcity-of-trust-in-the-age-of-ai/ https://welfarefootprint.org/2026/02/04/the-scarcity-of-trust-in-the-age-of-ai/#respond Wed, 04 Feb 2026 22:36:12 +0000 https://welfarefootprint.org/?p=11429
The Scarcity of Trust in the Age of AI: Why Human Expertise Anchors the Welfare Footprint Wladimir J. Alonso In a recent analysis, Nate B. Jones, a sharp observer of … Continue reading The Scarcity of Trust in the Age of AI
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The Scarcity of Trust in the Age of AI: Why Human Expertise Anchors the Welfare Footprint

Wladimir J. Alonso

In a recent analysis, Nate B. Jones, a sharp observer of the evolving landscape of artificial intelligence, posed a compelling diagnosis: as AI creates an abundance of content, it simultaneously creates a scarcity of trust.

In “Why the Smartest AI Bet Right Now Has Nothing to Do With AI,” Jones does not argue that value lies in producing more information. Instead, he points out that as the cost of generation collapses, the real bottleneck shifts elsewhere: to credibility, accountability, and trustworthy judgment. He describes a future where organizations that function, in effect, as “trust banks”—institutions that can authenticate, certify, and provide a reliable signal amidst the noise—are the ones that endure.

The Currency of Credibility

This framing resonates deeply with our work at the Welfare Footprint Institute. We attempt something inherently demanding: quantifying lived affective experiences (Pain and Pleasure) across species and systems to guide real-world decisions. In this domain, trust is not optional. If our estimates are not credible, they cannot inform policy, advocacy, or reform—and suffering remains unchanged.

Jones’ core insight applies directly here: AI dramatically lowers the cost of producing analyses, narratives, and even data-like outputs. But it does not lower the cost of being right. If anything, it raises the premium on institutions and people willing to say, “This is our estimate, and we stand behind it”, and to bear responsibility for that claim.

Our Strategy: Judgment Over Generation

Connecting this “trust deficit” to our own work clarifies why we operate the way we do. Our strategy is built around accountable human judgment, empirical evidence, rigorous scientific methods, and radical transparency.

In an era where plausible text, figures, and explanations can be hallucinated in seconds, we invest in the slow, difficult work of validation. Every estimate we publish is grounded in evidence, documented assumptions, and expert scrutiny. That is not an inefficiency of our process; it is the core of its value.

The Role of AI: Powerful, but Not in Charge

None of this reflects pessimism about AI. Quite the opposite. We see AI as an extraordinary opportunity to augment human capacities—much like calculators, computers, and the internet did before it. Tools do not bear responsibility. Humans do.

We use AI to assist and to explore: to accelerate literature synthesis, test hypotheses, and prototype analyses. For example, we have developed several AI-assisted tools—including custom GPTs such as Hedonic-Track, Zootechnical Mapper, Interspecific Affect, and the Affect Map—designed to support expert-driven welfare analysis within the Welfare Footprint Framework. These systems help us think faster and broader, but they do not replace judgment.

We do not outsource responsibility to algorithms. At the end of the chain, humans remain the ones accountable for what is claimed, measured, and acted upon. As Jones notes in the video, knowing which option is right—and being willing to stand behind it—remains human terrain. In a world of artificial abundance, the most valuable resource is still authentic, accountable judgment. That is the foundation on which the Welfare Footprint analyses are built.

Finally, this is not incidental: for those who watch the interview in full, the video also contains  sharp insights on career strategy and entrepreneurship—particularly on how to build durable value in a world saturated with AI-generated output.

]]> https://welfarefootprint.org/2026/02/04/the-scarcity-of-trust-in-the-age-of-ai/feed/ 0 Stephan Jockel Arbeitsbereich der Inhaltserschließung in der Deutschen Nationalbibliothek in Frankfurt am Main The Gift of Clarity: making sense of animal welfare with the Welfare Footprint Framework https://welfarefootprint.org/2025/12/19/welfare-footprint-made-simple/ https://welfarefootprint.org/2025/12/19/welfare-footprint-made-simple/#respond Fri, 19 Dec 2025 19:34:02 +0000 https://welfarefootprint.org/?p=11323
The Gift of Clarity: making sense of animal welfare with the Welfare Footprint Framework Wladimir J Alonso, Cynthia Schuck-Paim As the year winds down, many of us find ourselves reflecting … Continue reading The Gift of Clarity: making sense of animal welfare with the Welfare Footprint Framework
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The Gift of Clarity: making sense of animal welfare with the Welfare Footprint Framework

Wladimir J Alonso, Cynthia Schuck-Paim

As the year winds down, many of us find ourselves reflecting on our ambitions, what we achieved, what we didn’t, and what we hope to do next. For Sarah (fictional), a veterinary student with big dreams, this reflection recently turned into a moment of uncertainty.

Sarah sat in the university library, the soft glow of her laptop illuminating a complex diagram. She didn’t just want to pass her exams; she wanted to contribute to science and tangibly improve the well-being of farm animals. She had discovered the Welfare Footprint Framework, a method for measuring welfare by quantifying the time animals spend in different intensities of pain and pleasure

The concept felt natural to her. After a recent bout of migraines, she understood firsthand that welfare boils down to the intensity and duration of the experience. Conversely, she knew the good times with her family and friends could be measured by the duration of her joy. 

But as she looked at the complete analysis diagram, her enthusiasm gave way to doubt.

The "All-or-Nothing" Fallacy

The diagram seemed to demand an understanding of entire production systems, including detailed lists of veterinary conditions, affective quantification, and epidemiological reviews. “Do I have to be an ethologist, a statistician, and a neuroscientist all at once?” she wondered. 

This diagram outlines the step-by-step process of calculating a Welfare Footprint, from describing living conditions to quantifying welfare per unit of animal product.

Sarah was about to give up when she realized she had misunderstood the assignment. The WFF wasn’t demanding she mapped the entire world at once; it was built to be modular, and scalable.

She discovered the power of “productive simplification”, ways to do high-impact research without the overwhelm. She realized she had at least two distinct choices to make her mark:

1. The “Building Block” Approach: Just as a recent study focused solely on air asphyxia in fish, she could quanitfy the impact of a single source of pain, like piglet castration. This single data point would be a Cumulative Pain figure for future analyses. It was a manageable and short project that would already produce useful and actionable insights.

2. The “Essential Footprint” Approach: Alternatively, she could map a whole system, like the impacts of puppy mills or intensive rabbit meat production, by accepting that her map would be “incomplete”. By focusing only on the dominant sources of pain and pleasure, she could create a “Version 1.0” that served as a foundation for others to refine.

Connection and Compassion

Sarah’s fear dissipated once she grasped that transparency was the key. As long as she clearly defined, and disclosed, what she had quantified, her contribution was valid.

She also realized she wasn’t alone. The self-contained nature of WFF problems makes them highly suitable for AI assistance. Using tools like the “AffectMap” and “HedonicTrack” could boost her productivity, allowing AI to handle the first round of heavy lifting while she focused on the irreplaceable human tasks of critical thinking,  sense-making, and the responsibility of drawing defensible conclusions from imperfect evidence.

Sarah launched her project, not attempting to map the universe, but a meaningful piece of it. She learned that you don’t need to be perfect to be effective.

As we head into the holidays, Sarah’s realization is a good reminder for all of us. Whether you are a student, a researcher, or an advocate, the goal isn’t to build the analysis of an entire system alone, but to contribute one solid brick to its foundation .

From all of us at the Welfare Footprint Institute, we wish you a restful break. May your holiday season be filled with high-intensity joy and zero duration of pain, the same balance we are all working to achieve for animals.

See you in the New Year!

]]> https://welfarefootprint.org/2025/12/19/welfare-footprint-made-simple/feed/ 0 Welfare Footprint Institute What we learnt from our recruiting process https://welfarefootprint.org/2025/09/26/what-we-learnt-from-our-recuriting-process/ https://welfarefootprint.org/2025/09/26/what-we-learnt-from-our-recuriting-process/#respond Fri, 26 Sep 2025 15:14:16 +0000 https://welfarefootprint.org/?p=11202
What We Learned from Our Shrimp Welfare Research Position Selection Process As the Welfare Footprint Institute grows, we are slowly moving from being just scientists running projects to organizers of … Continue reading What we learnt from our recruiting process
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What We Learned from Our Shrimp Welfare Research Position Selection Process

As the Welfare Footprint Institute grows, we are slowly moving from being just scientists running projects to organizers of a broader effort. That comes with responsibility: the mission to measure suffering so it can be reduced on a global scale is not a small task. To do it well, we need more than methods and results—we need people. And bringing in the right people is one of the hardest and most important decisions any organization can make.

Our recent hiring process for a research position in shrimp welfare brought all of this into focus. We received far more interest than we expected, and we also faced a challenge that is becoming common everywhere: AI now shapes how people apply for jobs, often in ways that are hard to recognize and harder to judge. We thought it was worth writing down what we learned—both to be transparent with the many excellent candidates we couldn’t hire, and to share insights that might help others who are building teams in this changing environment.

the response

When we opened applications, 247 people from around the world applied (!). For a specific topic, that number surprised us. What stood out was not just the size of the pool, but the fact that so many applicants were ready to engage seriously with the welfare of arthropods—an area that, until recently, few would consider as part of mainstream animal welfare science. It was encouraging to see such knowledge, interest, and passion directed to this novel field.

the role of ai

We also recognize that most candidates will use AI tools—and we’re not only okay with that, we see it as increasingly essential. Fluency in leveraging AI can expand one’s capacity for reasoning, literature reviews, hypothesis generation, and more. In fact, as highlighted in this video, the real discriminant is not whether someone used AI, but how they used it: Did it support their thinking or merely replace it? We knew what the AI answers would be (we ran several potential questions in ChatGPT, Claude, Gemini, Grok, Perplexity, and Consensus), so in our process, we tried to design evaluations so that genuine thought, not just carefully constructed sentences assisted by AI, could emerge.

how we ran the process

To keep the evaluation fair, we ran the first two stages blind. Reviewers did not see names, institutions, or CVs—only answers to the questions. Candidates’ backgrounds were revealed only after Stage 2, once they had also completed a paid task.

This decision mattered. Although we had no specific expectations or requirements in mind when we first started, it became clear through the selection process that the ability to reason well about shrimp welfare did not necessarily depend on having a predictable academic or career profile. If we had started with CVs, we probably would have filtered out strong candidates who didn’t fit the “expected” mold. 

Stage 1: Screening

The first stage was a short written exercise: five questions on shrimp neurobiology, allostatic overload, the limits of mortality as a welfare measure, vulnerability traits in shrimp farming, and decision-making under conflicting evidence. Out of 247 candidates, 24 advanced (about 10%).

The difference wasn’t simply right versus wrong. Many candidates gave accurate answers, but the strongest stood out because of their originality and depth. They cited relevant papers, drew on personal observations, questioned the framing of the questions, proposed original approaches and were specific about parameter values. They acknowledged gaps in the literature, and pointed to practical constraints likely to emerge in commercial farming operations. That extra layer of reasoning was what made the difference.

Stage 2: Applied Questions

In Stage 2, candidates designed welfare indicators for shrimp in biofloc systems and created a classification system for pain caused by skin injuries in salmon. These questions pushed people to move from describing problems to proposing solutions that could actually be used in practice.

Most submissions were strong. What set the finalists apart were some of the following elements:

  • Skepticism when needed, but also constructive proposals
  • Clear definitions that could be measured consistently
  • Specific numbers, ranges, or thresholds instead of vague terms
  • Transparency about uncertainty
  • New frameworks and indicators proposed
  • Awareness of how exactly things would actually work on farms, with real costs and constraints

By contrast, some otherwise excellent answers left key elements underspecified—for example, naming a promising indicator but not explaining how to measure it. At this stage, such details made the difference.

Still, the decision was painful. Many of the 21 candidates who didn’t advance had PhDs, long lists of publications, or substantial field experience. Their answers were thoughtful, and we would have been glad to work with them if we had more positions available. 

lessons we carry forward

A few things became clear from this process:

  • Biological knowledge alone isn’t always enough; the ability to turn it into usable knowledge is just as important. Such an ability may come from candidates with traditional or more varied backgrounds.
  • The best responses often came from people who combined different perspectives—physiology, behavior, aquaculture practice, engineering, data analysis.
  • Although this was not a requirements, candidates with hands-on experience brought insights in terms of feasibility of the solutions proposed that pure academics sometimes missed, like the operational trade-offs of implementing new measures. 
  • Running the review blind, and only looking at CVs after the applied stage, was crucial. It allowed us to focus on the work itself, free from assumptions about specific backgrounds.

looking ahead

This won’t be our last hire, and we’ll build on what we learned here. Above all, we remain grateful to everyone who applied. This was our first major recruitment process, and the experience taught us valuable lessons about both evaluation and logistics that will benefit future applicants. The interest and quality we saw also reminded us that the field of animal welfare is rich with talent and commitment. Deciding among so many strong applications was one of the hardest things we’ve had to do—and we deeply respect those who joined us in this process, even if only one person could be chosen. 

For those still interested in this work, stay connected. We’ll have more positions, and the field itself is expanding beyond just our institute. The fact that 247 people wanted to work on shrimp welfare—something that barely existed as a research area five years ago—suggests this community will keep growing. We hope to be part of making space for more of this talent, whether directly through our own hiring or by helping demonstrate that this work matters.

]]> https://welfarefootprint.org/2025/09/26/what-we-learnt-from-our-recuriting-process/feed/ 0 Welfare Footprint Institute The Welfare Footprint of the Egg https://welfarefootprint.org/2025/07/23/the-welfare-footprint-of-the-egg/ https://welfarefootprint.org/2025/07/23/the-welfare-footprint-of-the-egg/#respond Wed, 23 Jul 2025 13:50:45 +0000 https://welfarefootprint.org/?p=10644
A Milestone in Animal Welfare Science: The Welfare Footprint of the Egg and the ISAE 2025 Workshop On August 4th, 2025, during the 58th International Society for Applied Ethology (ISAE) … Continue reading The Welfare Footprint of the Egg
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A Milestone in Animal Welfare Science: The Welfare Footprint of the Egg and the ISAE 2025 Workshop

On August 4th, 2025, during the 58th International Society for Applied Ethology (ISAE) Congress in Utrecht, the Welfare Footprint Institute will host a full-day (10:00-16:00) satellite workshop titled “The Science of Welfare Footprinting: The Welfare Footprint of the Egg.” This event marks a milestone in the evolution of animal welfare science—bringing together the launch of a new scientific tool and the publication of its first major application.

At the center of this event is The Welfare Footprint of the Egg, a forthcoming volume co-edited by Cynthia Schuck-Paim, Kate Hartcher, and Wladimir J. Alonso, and published by CRC Press (Taylor & Francis Group). This book is the first comprehensive application of the Welfare Footprint Framework to an entire animal production system. It represents the culmination of the collaborative work involving over 60 experts in welfare science, poultry welfare, epidemiology, veterinary science, and data analysis.

The central aim of this book is to quantify what egg-laying hens and other animals involved in the egg production chain actually experience over the course of their lives—and to do so in a way that is scientifically rigorous, transparent, and comparable. In doing so, the Welfare Footprint Framework breaks new ground by directly measuring what matters most to the animals themselves: their affective experiences. This is done through systematic assessment of how long and how intensely animals experience states such as pain, fear, discomfort, or—on the other end of the spectrum—relief and pleasure.

The book covers the entire egg production chain, including commercial laying hens, breeding flocks, culled chicks, and animals lost to disease or injury. It integrates epidemiological data on the prevalence of welfare conditions, expert-driven classification of pain intensities, and system-level modeling to calculate cumulative welfare impacts. These impacts are expressed using biologically meaningful metrics: the total time animals spend in affective states, positive and negative, of varying intensities. These results are then standardized per unit of product, allowing for direct comparison across production systems, including caged and cage-free systems.

Beyond its immediate findings, The Welfare Footprint of the Egg lays the foundation for how animal welfare is assessed and communicated. It is the first in a planned series of volumes applying the same methodology to other animal production systems, such as broiler chickens, pigs, and farmed fish. Its publication signals a new phase for considering animal welfare in multiple contexts—one that brings together biological realism, analytical rigor, and practical relevance for decision-making in policy, industry, advocacy, and consumer behavior.

The upcoming workshop at ISAE 2025 is planned to be both intellectually engaging and practically applicable,  guiding participants through the core concepts of the Welfare Footprint Framework, showcasing tools for analyzing affective experiences, and offerign space for collaborative initiatives.

To learn more about the event and how to register, please visit the ISAE 2025 Website. You can register only for the sattelite workshop (cost is 30 euros, which covers lunch and coffee breaks), or the full conference, check out the full programme! 

 

]]> https://welfarefootprint.org/2025/07/23/the-welfare-footprint-of-the-egg/feed/ 0 Welfare Footprint Institute Unexpected Global Response to WFF Study on Trout Slaughter and Welfare Impact https://welfarefootprint.org/2025/06/26/unexpected-global-response-to-wff-study-on-trout-slaughter-and-welfare-impact/ Thu, 26 Jun 2025 14:14:06 +0000 https://welfarefootprint.org/?p=10386
Unexpected Global Response to WFF Study on Trout Slaughter and Welfare Impact In just three weeks since its publication on 5 June 2025, our latest study in Scientific Reports has … Continue reading Unexpected Global Response to WFF Study on Trout Slaughter and Welfare Impact
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Unexpected Global Response to WFF Study on Trout Slaughter and Welfare Impact

In just three weeks since its publication on 5 June 2025, our latest study in Scientific Reports has drawn attention far beyond our expectations. According to the journal’s metrics the article has already reached over 12,000 accesses and recorded an Altmetric score of 416—an exceptional level of engagement for a newly published scientific paper.

Even more surprising is the widespread and international media coverage it has received, with the study being reported across major science platforms and local news outlets in multiple countries. This response underscores a growing global interest in the welfare of aquatic animals—a subject too often overlooked in mainstream discourse.

At the heart of the paper is the application of the Welfare Footprint Framework (WFF) to quantify, in time-based terms, the distress experienced by rainbow trout when slaughtered by air asphyxiation—a method still widely used in fisheries and aquaculture. The findings are sobering: each trout endures an estimated 10 minutes of moderate to extreme pain (with a credible range of 1.9 to 21.7 minutes) during the slaughter process. Standardized per kilogram, this equates to an average of 24 minutes of significant pain per kg, with upper estimates reaching 74 minutes/kg.

One of the most promising contributions of the study lies in its cost-effectiveness modeling. We found that investment in stunning equipment—if applied effectively—could avert between one and twenty hours of moderate to extreme pain per USD of capital cost. Yet these benefits depend on reliable implementation. In practice, many commercial operations continue to face difficulties with stunning—due to equipment variability, improper positioning, or lack of procedural oversight—often reducing the expected welfare gains.
Perhaps even more critical are the pre-slaughter stressors: crowding, transport, and handling may expose fish to prolonged distress lasting hours or even days—potentially outweighing the pain and distress experienced during slaughter. Fortunately, the WFF is designed to assess these phases as well, helping identify the most impactful intervention points to reduce suffering across the entire production chain. Ongoing work is quantifying now the welfare impacts of these preslaughter phases.

The rapid and widespread interest in this study reaffirms that animal welfare matters deeply to a growing segment of the public. It also highlights the power of a scientifically grounded framework—like the Welfare Footprint Framework—to translate complex, subjective experiences into easy-to-understand and actionable data that can inform more compassionate decisions.

]]> Welfare Footprint Institute Defining Affective Experiences https://welfarefootprint.org/2025/04/11/defining-affective-experiences/ https://welfarefootprint.org/2025/04/11/defining-affective-experiences/#respond Fri, 11 Apr 2025 18:26:48 +0000 https://welfarefootprint.org/?p=10268
How to Define Affective Experiences for Analysis: Striking the Right Level of Detail  Cynthia Schuck-Paim & Wladimir Alonso The Welfare Footprint Framework (WFF) is designed to quantify the welfare impact … Continue reading Defining Affective Experiences
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How to Define Affective Experiences for Analysis: Striking the Right Level of Detail

 Cynthia Schuck-Paim & Wladimir Alonso

The Welfare Footprint Framework (WFF) is designed to quantify the welfare impact of each and every affective experience that the target animals endure over a timeframe of choice, as measured in terms of its intensity (how pleasant, painful, frustrating, or distressing it is) and duration (how long it lasts). The goal is to understand and compare the welfare of animals (i.e., the cumulative time spent in affective states of different intensities) across conditions and systems in a way that is both scientifically sound and useful. To do this, the WFF relies on a set of building blocks (note: we use initial capital letters for terms with operational definitions, described in this glossary):

  • Circumstances: the environmental and management conditions that animals encounter
  • Biological Consequences: the immediate consequences resulting from an organism’s interaction with Circumstances. They can be positive or negative, physical or psychological (perceptual, motivational). Not all Biological Consequences are affectively relevant: for example, immune suppression is not ‘felt’, but is an intermediate step that may lead to an affectively relevant Biological Consequence (a painful disease). Each Biological Consequences may unfold differently in different animals, leading to a different Affective Experience
      • Pathways:. different progressions in which outcomes may unfold over time. Common Pathways include: Acute vs. chronic, Fatal vs. non-fatal.
      • Severity: scale of the biological condition, defined based on clinical or observable criteria (e.g., wound depth, disease stage, gait score)
      • Degree of Deprivation: the extent to which a behavioral or physiological motivation is thwarted (e.g.,  access to an unsuitable nest or no nest)
      • Degree of Fullfilment: the extent a behavioral or physiological motivation is satisfied (e.g., foraing in innert vs rewarding substrate). 
  • Affective Experiences: These are the felt states that the WFF aims to quantify—such as pain, fear, frustration, comfort, pleasure, or satiety.

In conducting any welfare impact analysis, one must first identify the multiple Affective Experiences that animals endure over their lives in the different scenarios analysed. The purpose of this text is to help users define which Affective Experiences should be analyzed and at what level of specificity. This involves finding a balance between biological plausibility, data availability, and the usefulness of the results.

How to Decide What Level of Detail Is Appropriate

One of the most critical steps in applying the Welfare Footprint Framework is deciding how Affective Experiences should be defined for analysis. This step determines the resolution of the welfare quantification—too broad, and important nuances may be lost; too detailed, and the analysis may become unmanageable or unsupported by data. The challenge lies in avoiding both oversimplification and unnecessary complexity.

Not enough detail

It is important to recognize that the appropriate level of detail will depend on the objective of the analysis. In some cases, the goal may require rapidly estimating the overall affective state of an animal during a given phase of life, such as the rearing or production phase. In such cases, it may be valid to use broad categories of experience—such as general discomfort or well-being—without distinguishing between every individual disease, deprivation, or positive event the animal may encounter. However, this type of broad estimation cannot be directly supported by empirical evidence on intensity and duration. That is because the scientific literature and field data provide validated indicators of intensity and duration only for specific conditions, such as fractures, respiratory infections, social bonding, or restricted movement.

To ensure that the analysis remains evidence-based, it is often necessary to define affective experiences at a more granular level. This means anchoring them in specific Biological Consequences and pathways that are known to result in distinct experiences. Pain from a fractured limb, pleasure from affiliative interactions, or fear from predator exposure are all examples of experiences with documented affective relevance and measurable properties. These experiences are not only more scientifically tractable—they also tend to mark events that are especially meaningful in the life of the animal. They help characterize key welfare risks and opportunities.

Too much detail

At the other end of the spectrum, one may be inclined to define affective experiences in an overly detailed manner. This often happens when attempting to distinguish between every minor variation in symptom presentation or disease etiology, believing that more granularity necessarily equates greater accuracy. However, excessive granularity can make the analysis cumbersome and can exceed the resolution supported by available data. For example, differentiating between every possible cause of lameness, or treating slight differences in respiratory pathogen strains as separate experiences, may not yield meaningful distinctions in what the animal feels.

A similar issue arises when users define affective experiences based on symptoms (e.g., fever, coughing). This approach may appear detailed or clinically grounded, but it introduces unnecessary complexity and limits the usefulness of the analysis. Symptoms can indeed be part of an affective experience—such as coughing that results from a painful airway irritation —but they are often not reliable units of analysis on their own. Symptoms reflect observable aspects of a disease process, and their presence may suggest a certain intensity or progression.

Moreover, symptoms (or indicators – see this figure) do not provide a dependable basis for quantification. There is often no validated data on the duration and prevalence of individual symptoms in a population. For example, we might know how many animals suffer from tracheitis, and for how long, but we typically lack systematic data on how many animals cough, how frequently, and for how long in a way that can be used for quantification. Because symptoms are part of the unfolding of a disease over time, they are best viewed as indicators or inputs—useful in clinical judgment or diagnostic pathways. Additionally, the same symptom can appear across a range of very different conditions, and its affective relevance depends entirely on context. Coughing could reflect a temporary irritation or a necrotizing infection. Treating symptoms as affective experiences overlooks this variability and masks the differences in what the animal may actually feel.

Classifications based on Aetiology

Another common tendency is to define Affective Experiences in terms of the specific aetiology of a condition—for example, as “respiratory disease caused by respiratory syncytial virus (RSV),” or “respiratory disease caused by influenza virus,” or “respiratory disease caused by metapneumovirus.” While this level of detail might reflect a clinical interest in diagnosis, it is not helpful for affective experience analysis. First, population-level data on the prevalence of these specific viral causes is rarely available, as diagnostic testing is typically not performed routinely, especially in large-scale production systems. Second, these aetiologies often result in highly similar symptoms, and the affective experience they produce (such as irritation, air hunger, or fatigue) is often indistinguishable. In human health, a common practice is to refer to these overlapping presentations as “Influenza-like illness” (ILI), recognizing that the syndrome is more relevant to the patient’s experience than the precise pathogen. The same logic applies here: rather than defining separate affective experiences by each pathogen, it is better to define them at the level of the syndrome or Biological Consequence they share. This allows for more realistic, evidence-based estimates of intensity and duration, and avoids artificial fragmentation of experience categories.

Some Useful Rules

Below we provide a set of principles to help users define affective experiences at a level of detail that is analytically sound, biologically meaningful, and operationally feasible for population-level welfare assessment

1. Group when experiences are similar in intensity and duration

Group Biological Consequences into one affective experience only when they are likely to feel the same to the animal. 

✅ Example: Several mild respiratory infections that all cause brief mucosal discomfort might be grouped.

2. Keep separate when affective experiences are qualitatively distinct

Even if two Biological Consequences affect the same body system, they should be treated as separate affective experiences if the animal is likely to feel them differently. 

✅ Example: Pain from a clean, well-aligned bone fracture should not be grouped with pain from a bone fracture with non-union that never heals and leads to chronic inflammation, even though both are classified as ‘bone fractures.’ The intensity of the pain caused by each type of fracture differ significantly. Defining them separately allows for more informative assessments, especially when comparing different housing systems, genetics, or management practices that affect the likelihood of one type over another.

3. Separate experiences that unfold differently in different animal groups

Even if animals share the same condition or behavioral opportunity, they may experience it differently depending on the pathway (e.g., acute, chronic, fatal), severity (e.g., mild vs. severe), degree of deprivation, or degree of fulfillment. Each distinct trajectory results in a different affective experience and must be analyzed as such.. 

✅ Example: Hens suffering from vent wounds due to pecking may follow different pathways: (1) Some heal spontaneously and experience short-term pain; (2) Others develop infections and chronic inflammation; (3) a few die due to complications from severe tissue damage. Each group must be modeled as having a distinct affective experience

✅ Example: All hens may have access to foraging, but the quality of the substrate matters: (1) Some peck at inert litter, receiving minimal sensory feedback, (2) Others forage in a rich substrate containing food particles and insects.

4. Base decisions on the level of detail supported by data

Sometimes, the theoretically ideal distinctions cannot be made because population-level data is not available. In such cases, broader groupings may be necessary.

✅ Example: Lameness in broilers can result from many causes. However, even when the underlying aetiology is the same, different birds may experience it in very different ways depending on the severity, location, and progression of the condition. Two animals with the same musculoskeletal issue might have completely different levels of discomfort, mobility restriction, or pain duration. Because welfare assessments often rely on gait scores, which integrate these aspects into an observable indicator, defining affective experiences based on gait score is both more feasible and more informative than trying to isolate every underlying etiology.

5. Make distinctions that are informative for welfare analysis

A key reason to define experiences separately is that it makes the quantification informative. If the goal is to compare the welfare consequences of two or more procedures, such as different vaccination methods, or environmental enrichments, the underlying affective experiences must be analyzed separately. Even if the tissue affected is the same or the outcome is similar in general terms, differences in how the condition is produced can lead to distinct experiences for the animal.

✅ Example: Pain from thermal dehorning with a hot iron should not be grouped with pain from chemical dehorning, even though both are procedures applied to the same area of the animal’s body. The mechanism of tissue damage, progression of healing, and likelihood of chronic pain can differ significantly between these techniques. Defining these experiences separately is also essential for evaluating and comparing their welfare consequences .

6. Avoid grouping based on symptoms alone

Symptoms such as coughing, lethargy, or poor feather condition may result from a wide range of Biological Consequences and should not be used as the basis for defining affective experiences. A clear diagnosis, including an understanding of cause, location, and mechanism, is needed.

❌ Less Appropriate: Grouping all cases involving coughing as “Pain from coughing.” 

✅ More Appropriate: Defining pain based on the underlying cause, such as “Pain from upper respiratory tract infection.”

]]> https://welfarefootprint.org/2025/04/11/defining-affective-experiences/feed/ 0 Welfare Footprint Institute Confounding Factors in Welfare Comparisons https://welfarefootprint.org/2025/03/14/confounding-factors-in-welfare-comparisons/ https://welfarefootprint.org/2025/03/14/confounding-factors-in-welfare-comparisons/#respond Fri, 14 Mar 2025 16:13:38 +0000 https://welfarefootprint.org/?p=10165
Confounding Factors in Welfare Comparisons of Animal Production Systems Wladimir J Alonso, Cynthia Schuck-Paim Controlled experiments—where variables are deliberately manipulated to establish cause-and-effect relationships—are the gold standard for drawing reliable … Continue reading Confounding Factors in Welfare Comparisons
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Confounding Factors in Welfare Comparisons of Animal Production Systems

Wladimir J Alonso, Cynthia Schuck-Paim

Controlled experiments—where variables are deliberately manipulated to establish cause-and-effect relationships—are the gold standard for drawing reliable conclusions in science. However, controlled experiments are not always feasible, ethical, or practical, and one must instead rely on retrospective analyses of existing datasets to uncover meaningful patterns.

This is particularly common in social sciences, psychology, public health, epidemiology, and economics, where large observational datasets are often used to infer causality. For example, in public health, studies on the long-term effects of smoking or the impact of pollution on respiratory diseases have relied on existing medical records and environmental data rather than randomized trials, which would be unethical. 

A classic example of how careful analysis of observational data can overturn conventional wisdom is the work of Daniel Kahneman, whose research on cognitive biases and decision-making (Thinking, Fast and Slow) demonstrated that humans do not always behave rationally, as previously assumed in classical economic models. His work, which earned him a Nobel Prize in Economics, has often relied on retrospective analyses of decision patterns rather than controlled trials.

Similarly, Hans Rosling, in Factfulness, debunked widespread myths about global poverty and development trends by analyzing historical datasets. His work demonstrated that when adjusted for factors like GDP growth, access to healthcare, and education levels, many claims about worsening global inequality were not supported by the actual data.

In economics, Thomas Sowell has extensively applied this principle in his analysis of income disparities, educational outcomes, and social mobility. His work demonstrates how controlling for factors like education level, work experience, and family structure can reveal patterns that differ from popular assumptions, emphasizing the need for data-driven, rather than narrative-driven, conclusions.

Transition to Animal Welfare Research

The same principles apply to animal welfare research, where controlled experiments are often unfeasible or unethical, and researchers must rely on industry data, automated monitoring systems, and observational studies to assess welfare outcomes. As the use of precision livestock farming and automated welfare monitoring technologies (such as computer vision tracking, behavioral sensors, and real-time physiological monitoring) expands, retrospective analyses of these large datasets will become even more critical in drawing valid conclusions about animal welfare in different production systems.

Without careful adjustments, comparisons may incorrectly attribute welfare outcomes to production systems rather than to confounding variables such as differences in management decisions, economic investment, types of animals or life-stage differences.

Here we describe some common confounding factors in welfare comparisons, and possible analytical approaches to address confounding.

Resource and Management Investment Disparities

If economic and human resources are allocated differently among production systems, welfare comparisons can be skewed. Feedlots, for example, typically invest more in veterinary care per animal than pasture-based systems. This can lead to lower mortality rates in feedlots, not because the system is inherently better for welfare, but because intensive monitoring and interventions can reduce the likelihood of acute health crises. However, these interventions may also mask underlying welfare issues of similar or worse severity in feedlots than in pasture-based systems.

ANIMAL SELECTION BIASES

Selection biases occur when different production systems use different genetic lines or selectively place healthier animals in specific systems. In the beef cattle industry, for example, grain-fed feedlot cattle are often selected from breeds with greater feed efficiency, or that, depending on market prices and the animals’ general health, are deemed worthy of the investment on a feedlot at the finishing phase. A comparison of welfare outcomes between these groups must therefore account for the pre-existing and underlying differences in the health and resilience between the two groups of animals.

Early Life Experience and System Adaptation

A critical confounding factor often overlooked is the animal’s developmental history and adaptation to specific environments. Production animals develop physiological and behavioral adaptations to their early rearing environments that significantly impact their ability to thrive when transferred to different systems later in life. Valid welfare comparisons must account for the congruence between rearing and production environments. A system that appears superior when populated with appropriately reared animals may perform catastrophically when housing animals with mismatched developmental histories. This understanding fundamentally challenges simplistic “system A versus system B” welfare comparisons. The welfare implications of any production system cannot be evaluated in isolation from the animal’s developmental trajectory. Meaningful comparisons must either match rearing environments to production systems or explicitly account for adaptation capacity as a welfare dimension in its own right.

Incomplete Welfare Outcome Coverage

A pervasive confounder in system comparisons is selective measurement of Biological Consequences. Studies often focus on easily quantifiable Biological Consequences like mortality, injuries, or production parameters while neglecting equally important Biological Consequences such as behavioral and social deprivations or long-term health impacts. This selective inventory creates fundamental distortions in system evaluations. For example, early weaning practices in pig production may appear advantageous when measuring immediate post-weaning mortality and growth rates, while missing significant Biological Consequences such as compromised gut development and impaired immune function. Valid welfare comparisons require clarity on the spectrum of Biological Consequences considered across all relevant welfare domains in all systems considered.

Environmental and Geographic Variability

Welfare comparisons across production systems are frequently confounded by unaccounted environmental and geographic factors that operate independently of the system design itself. Production systems situated in different regions experience fundamentally different environmental challenges that significantly impact animal welfare outcomes. Climate variations, for instance, can affect the welfare implications of outdoor systems—the same pasture-based approach might provide excellent welfare in moderate climates but create severe thermal stress in regions with extreme temperatures. Valid welfare comparisons must therefore either control for environmental variables by comparing systems within similar geographic regions or explicitly incorporate environmental challenges as contextual factors in the analysis. 

TEMPORAL CONSIDERATIONS

Welfare comparisons across systems that raise animals with different lifespans present another challenge. Short-lived production animals may experience fewer cumulative welfare issues simply because they do not live long enough for severe outcomes to develop, or because their shorter lifespan leads to less time spent in negative states. This can create a false perception that high-turnover systems lead to better health than systems where animals live longer. One solution is to compare the quality of life of animals on an average day. Where life changes over the production cycle, average day life quality can be assessed at different points in time. The average day approach normalizes welfare assessments across a standard time unit, ensuring that the overall quality of life is assessed rather than just its duration.

Examples of Confounding in Comparisons of Production Systems

laying hens

In a meta-analysis of 6,040 commercial laying hen flocks across 16 countries, which examined mortality rates in conventional cages, furnished cages, and cage-free aviaries, we found that when the maturity of the system was controlled (i.e., the level of experience with the system), there were no significant differences in mortality between caged and cage-free systems. This finding challenges the previously widespread belief that cage-free systems inherently have higher mortality rates. Instead, our analysis demonstrated that the learning curve associated with managing these systems plays a crucial role in determining welfare outcomes.

salmon aquaculture

Developmental history significantly confounds welfare comparisons across production systems. A clear example appears in salmon aquaculture, where fish reared in controlled Recirculating Aquaculture Systems (RAS) develop fundamental physiological and behavioral deficiencies that compromise their resilience in natural environments, where environmental variation and pathogen exposure are greater. When RAS-reared salmon are transferred to sea net pens, they can experience severe welfare outcomes including osmotic stress, immune suppression, and high mortality rates—not because sea pens are inherently worse for welfare, but because these fish are physiologically underprepared for marine environments. Their development in highly controlled conditions has effectively prevented the formation of crucial biological coping mechanisms necessary for survival in natural settings. This represents not merely a system quality difference but a permanent developmental limitation. Any valid comparison between RAS and sea net pen welfare outcomes must recognize that apparent system differences often reflect not just adaptation failures but irreversible developmental impairments created by early rearing conditions.

beef cattle

  • Feedlot vs. Pasture-Based Systems: some comparisons may suggest that cattle finished in feedlot systems experience lower disease or mortality rates than cattle in pasture-based systems. However, this does not necessarily mean that feedlot conditions are better for welfare. Feedlot cattle are more intensively monitored and more likely to receive immediate earlier veterinary interventions, which can prevent disease progression and reduce acute disease-related deaths. A proper comparison would need to control for differences in veterinary care access and breed differences to determine whether potential welfare differences are inherent to the finishing system itself or to these confounding variables.

     

     

  • Genetic Selection: Different genetic lines may influence the welfare outcomes observed in different production systems. For example, some high-performance beef cattle breeds used in feedlots, such as Charolais or Belgian Blue, have been intensively selected for rapid muscle growth, which can lead to increased musculoskeletal issues, heat stress susceptibility, and calving difficulties (dystocia). Conversely, breeds commonly raised in pasture systems, such as Herefords or Brahman-crosses, are often selected for heat tolerance, parasite resistance, and lower maintenance needs, which may contribute to better welfare outcomes in extensive systems. If individuals appear healthier in one system, this could be due to their genetic traits for resilience rather than the system setting itself. Controlling for breed differences allows for a fairer assessment of the welfare impacts of each system.

PIGS

Mortality Rates in Farrowing Crates vs. Loose-Housing Systems: Some studies suggest that piglet mortality is lower in farrowing crates compared to loose-housing or outdoor systems, leading to the assumption that farrowing crates provide better welfare. However, this interpretation overlooks the welfare trade-offs for both the sow and piglets. Farrowing crates severely restrict the sow’s movement, preventing natural maternal behaviors such as nest-building and bonding with piglets, critical for sow welfare. Piglets raised by less-stressed mothers and with greater opportunities for behavioral expression may also benefit from greater resilience later in life, social coping ability and immune competence, leading to overall better welfare. This is also in line with research showing that improving maternal welfare improves disease resistance, and resilience of piglets.

ANALYTICAL APPROACHES ADDRESSING COUNFOUNDING

Addressing confounding factors is crucial in animal welfare research to ensure meaningful results. Several approaches can be employed:

  • Best-vs.-Best Comparisons: Compare each system under optimal conditions with equivalent resource investment to highlight the true potential welfare outcomes of each system.
  • Worse-vs.-Worst Comparisons: Examine systems under minimal resource investment to understand their resilience to neglect or poor management.
  • Distribution Analysis: Evaluate the full spectrum of welfare outcomes within each system, identifying whether one system consistently produces better outcomes or merely reduces worst-case scenarios.
  • Economic Equivalence Approach: Compare systems at equivalent financial investment levels to assess what level of welfare can be achieved within real-world economic constraints.
  • Multivariate Regression Analysis: Use statistical models to adjust for multiple confounding variables simultaneously, allowing for the isolation of the effect of the primary variable of interest
  • Propensity Score Matching: Match subjects based on the probability of receiving a treatment given their covariates, aiming to reduce bias due to confounding variables. 
  • Randomization: Randomly assign subjects to treatment or control groups to ensure that confounding variables are equally distributed, thereby minimizing their impact
  • Blocking: Arrange experimental units into groups (blocks) that are similar based on certain variables to control for variability and reduce potential confounding.
  • Negative Controls: Use negative control groups or outcomes to detect the presence of confounding variables and assess the specificity of the observed associations

CONCLUSION

Within the Welfare Footprint Framework, the development of a proper veterinary inventory plays a crucial role in identifying the Biological Consequences that arise from various Circumstances animals experience. However, establishing causal relationships between these Circumstances and the Biological Consequences identified is challenging due to multiple confounding variables—elements that can obscure or distort true causal links. Recognizing this, we place special emphasis on controlling for confounders.

Animal welfare research must adopt transparent analytical approaches to ensure robust conclusions. Without such rigor, comparisons between production systems risk being biased by resource disparities, genetic differences, lifespan variations, differences in experience with the systems and  environmental factors, or mismatches of rearing and growing conditions, leading to misleading conclusions. This review serves as a reminder that carefully accounting for these variables is not just an academic necessity but a practical imperative. By employing well-controlled comparisons, we can attain a more accurate and meaningful understanding of animal welfare impacts across different production systems. 

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Borrowing the Knowns and Unknowns Framework to Reveal Blind Spots in Animal Welfare Science​ Wladimir J Alonso, Cynthia Schuck-Paim There is always a risk of fixating on what we already … Continue reading Borrowing the Knowns and Unknowns Framework to Reveal Blind Spots in Animal Welfare Science
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Borrowing the Knowns and Unknowns Framework to Reveal Blind Spots in Animal Welfare Science​

Wladimir J Alonso, Cynthia Schuck-Paim

There is always a risk of fixating on what we already know while overlooking aspects of reality that might be equally or more important. Even science is not immune to this issue: often, research efforts—spanning years of publications and theses—make little meaningful progress because they remain confined to a “comfortable zone,” focusing on what is convenient to study rather than what is truly needed. 

Donald Rumsfeld famously introduced the Knowns Unknowns framework for categorizing knowledge during his tenure as U.S. Secretary of Defense, specifically in the context of addressing uncertainty about whether Iraq possessed weapons of mass destruction. Its roots trace back to earlier ideas in philosophy, psychology, and risk management: for instance, the Socratic concept of ignorance, which highlights the importance of acknowledging what we don’t know, and the Johari Window (1955), which categorizes knowledge about ourselves into what is known and unknown to oneself and others. In military strategy and decision-making, the framework assists in pinpointing both anticipated and unforeseen risks.

We believe this approach is equally valuable for animal welfare science. To counter this, one of the key components of the Welfare Footprint Framework is identifying putatively neglected sources of pain before describing and quantifying them. In this context, the Knowns and Unknowns framework serves as a valuable reminder to continuously question and challenge what we think we know. We share this perspective because we believe that each quadrant of this framework can be a useful tool for others studying or working to improve animal welfare.

Figure 1. The Knowns and Unknowns Framework organizes knowledge into four quadrants: Known Knowns, where information is well-documented; Known Unknowns, where we acknowledge there are gaps in our understanding; Unknown Knowns, where knowledge exists but is not fully recognized or applied; and Unknown Unknowns, representing areas where we are unaware of what we don’t know

1. Known Knowns: What We Know We Know

In animal welfare, Known Knowns are the well-established issues that have been thoroughly researched and widely acknowledged. These are problems where the causes, consequences, and solutions are clear.

A prime example is lameness in cattle, which has been extensively studied. The circumstances causing lameness—such as poor housing conditions, inadequate hoof care, and inappropriate flooring—are well understood. Its Biological Consequences, including chronic pain, reduced mobility, and decreased productivity, are similarly well-documented. Furthermore, solutions such as better housing designs, regular hoof trimming, and improved flooring are widely recognized as effective interventions.

The challenge for Known Knowns lies in ensuring consistent application of solutions across production systems. While the science is settled in many cases, practical implementation often lags due to economic or logistical constraints.

Figure 2. This diagram illustrates the Knowns and Unknowns Framework applied to animal welfare science, with each quadrant representing a different category of knowledge: Known Knowns, where we have a clear understanding of issues like cattle lameness, depicted by a cow struggling to walk; Known Unknowns, where we acknowledge gaps in understanding, like the welfare of salmon; Unknown Knowns, where existing knowledge such as the brain’s role in emotion is not fully utilized, shown by a mouse brain’s sagittal plane; and Unknown Unknowns, symbolized by a door, suggesting hidden or yet-to-be-discovered welfare concerns. This diagram serves as both a conceptual map and a call to action for ongoing research and application in improving animal welfare.

2. Known Unknowns: What We Know We Don’t Know

Known Unknowns in animal welfare refer to problems that are recognized but not yet fully understood or addressed. These issues highlight critical gaps in data or knowledge that must be addressed to develop effective solutions.

For example, many of the circumstances that trigger different affective states in fish are Known Unknowns. While research has established that fish can experience physical pain and other negative affective states, including fear and anxiety, our understanding on how various circumstances—such as poor water quality, crowding, or sudden environmental changes— ultimately translate into negative affective experiences remains limited. Specifically, the relationship between the perception of these circumstances by fish and the physiological and affective consequences they endure is not yet fully mapped.

3. Unknown Knowns: What We Don’t Realize We Already Know

Unknown Knowns represent knowledge that exists but has not been fully integrated into animal welfare science or applied in practice. These gaps often arise due to insufficient communication between researchers in different fields and, ultimately, scientists and animal advocates.

One particularly striking example is the neurological basis of affective states in animals. Pioneering neuroscientists such as Jaak Panksepp and Mark Solms have conclusively demonstrated that emotions like fear, joy, and pain originate from specific areas of the brain—particularly the primitive brainstem and midbrain structures—which are shared across a wide range of species. This robust body of evidence provides a clear foundation for understanding the emotional capacities of animals, yet it has not been fully embraced or utilized to comprehensively map these capacities in welfare assessments.

This gap in integration has significant implications for animal welfare. The ability to compare the capacity to experience affective states across species is fundamental to determining the moral weight of their suffering. It is also an important element for calculating the Welfare Footprint of animal products when interspecific considerations are involved. For example, weighing the welfare impacts of farming chickens versus salmon requires a clear understanding of potential differences in their emotional capacities (should one hour of Disabling pain have the same moral weight in both species? Can those groups of animals actually experience pain at those levels?).

Recognizing the importance of this issue, we at the Welfare Footprint Project are actively examining how this knowledge can be applied to interspecific welfare comparisons. This will be a critical component of future contributions, where we aim to provide practical methodologies for incorporating these insights as transparent, optional post-quantification adjustments (Interspecific Scaling) when conducting comparative welfare assessments.

4. Unknown Unknowns: What We Don’t Know We Don’t Know

Unknown Unknowns are the greatest blind spots. In animal welfare science, these represent welfare issues that have not yet been detected or understood, often due to limitations in current methodologies, gaps in data collection, or the inherent complexity of biological systems. By their very nature, these issues are difficult to predict or define, but they hold the potential to profoundly impact the welfare of animals.

While the specifics of these Unknown Unknowns remain speculative, parallels from human medicine might provide valuable insights. In humans, countless illnesses and conditions manifest subtly or remain undiagnosed due to diffuse and variable symptoms or the lack of adequate diagnostic tools. It is reasonable to assume that many conditions and forms of suffering in animal populations remain similarly hidden or inaccessible to current methods of observation and analysis.

It is interesting to note that this is an area where artificial intelligence (AI) may be of great value. AI excels at analyzing vast and complex datasets, uncovering patterns and anomalies that might otherwise escape human observation. By integrating data from multiple welfare indicators—such as behavioral changes, physiological markers, and environmental risks—AI can detect early warning signs of poor welfare and identify previously unknown patterns of suffering.

For example:

  • Subtle Behavioral Indicators: AI can analyze video footage to detect micro-behaviors that suggest stress or discomfort, even before these behaviors escalate into more obvious manifestations.
  • Unseen Physiological Trends: By processing biometric data, AI can identify trends and correlations indicative of pain or systemic illnesses that might not be visible through traditional monitoring methods.
  • Unforeseen Risks in Farming Systems: AI can model and simulate the long-term effects of farming innovations or environmental changes, predicting potential welfare risks before they emerge.

An Important Way to Address Unknowns: Transparency About the Limitations of Results

It is likely that the most significant sources of welfare loss in the most extensively studied farm species have already been identified. These include chronic pain caused by ailments associated with rapid growth and excessive productivity, chronic social stress from overcrowding, and painful routine procedures like tail docking, surgical castration, and beak trimming. However, even in these well-researched areas, we must remain vigilant. Surprising findings continue to emerge. For instance, in our analysis of broiler chickens, we initially assumed that their primary welfare burden, for individual chickens, would stem from physical ailments like lameness. However, we found that chronic hunger among breeder chickens—who are deliberately underfed to control growth—was actually the most significant source of welfare loss.

In pursuing the main goal of the Welfare Footprint Project, how do we account for the limitations of what we don’t yet know? When quantifying the Welfare Footprint of specific practices, production systems, or animal-based products, how can we ensure that potentially critical welfare aspects are not unintentionally overlooked?

Our solution is radical transparency regarding the scope and limitations of each analysis. Given the complexity of animal production systems, it is crucial to explicitly declare which key aspects—such as Life-Fates, Life-Phases, and Affective States—were included in the Welfare Footprint analysis. By doing so, we also make it clear what has been left out, ensuring that any potential gaps in knowledge are visible, rather than implicit or hidden.

For example, in our notation system (which we will detail in a future post), the Welfare Footprint of an egg will indicate:

  • Which Life-Fates were considered (e.g., laying hens, parent stock, and male chicks).
  • Which Life-Phases were included (e.g., from birth through the end of life).
  • Which experiences of pain and pleasure were accounted for.

By making these factors explicit, we enable meaningful comparisons between results and ensure that the scope of each analysis is clearly defined. Just as importantly, this approach creates transparency about what was left out, allowing researchers, policymakers, and advocates to recognize these Unknowns and work toward addressing them in future assessments. In doing so, we strengthen the scientific and ethical foundation of welfare evaluations, ensuring that no source of suffering goes unnoticed simply because it was not yet considered.

]]> https://welfarefootprint.org/2025/02/13/borrowing-the-knowns-and-unknowns-framework-to-reveal-blind-spots-in-animal-welfare-science/feed/ 0 Welfare Footprint Institute Using the Welfare Footprint Framework to document animal welfare in legal settings https://welfarefootprint.org/2024/12/17/using-the-welfare-footprint-framework-to-demonstrate-animal-abuse-in-legal-settings/ https://welfarefootprint.org/2024/12/17/using-the-welfare-footprint-framework-to-demonstrate-animal-abuse-in-legal-settings/#respond Tue, 17 Dec 2024 18:49:21 +0000 https://welfarefootprint.org/?p=9645
Using the Welfare Footprint Framework to Document Animal Welfare in Legal Settings The Welfare Footprint Framework (WFF) was originally developed to quantify and compare animal welfare impacts across different settings … Continue reading Using the Welfare Footprint Framework to document animal welfare in legal settings
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Using the Welfare Footprint Framework to Document Animal Welfare in Legal Settings

The Welfare Footprint Framework (WFF) was originally developed to quantify and compare animal welfare impacts across different settings and production systems. However, its structured approach to documenting pain intensity and duration makes it particularly valuable for another critical application: providing courts with systematic, evidence-based documentation of animal suffering in cruelty cases.

Many legal systems require demonstrating that animals experienced prolonged and severe suffering to establish criminal liability in cruelty cases. This can be challenging, as assessments of suffering have traditionally relied heavily on expert opinion, which may vary between evaluators and be viewed as subjective in legal settings.

The WFF addresses this challenge through its systematic approach to documenting evidence of suffering. By breaking down experiences into discrete time segments and clearly defining different intensities of pain, the framework enables a structured evaluation of: (1) The duration of different negative experiences; (2) The intensity of suffering during each phase; (3) The scientific evidence supporting these assessments; (4) The cumulative impact on the animal over time

For example, in cases involving severe neglect, the framework can help document (1) How long animals went without food or water; (2) The intensity of their suffering during different phases (of starvation/dehydration); (3) scientific evidence of their distress (neurological, physiological, behavioral, pharmacological, etc), (4) The cumulative time spent in different states of suffering.

Rather than relying solely on expert opinion, the WFF requires documenting specific evidence that supports or contradicts each assessment of pain intensity. This evidence-based approach helps demonstrate to courts that assessments of suffering are grounded in scientific findings rather than subjective judgment.

The framework’s potential in legal settings was recently explored in a workshop with Costa Rica’s National Animal Health Service (SENASA) and Judicial Investigation Agency (OIJ). Over two days, professionals from both agencies examined how the WFF could be applied to strengthen documentation of animal suffering in legal proceedings. The structured nature of the framework resonated with participants as a way to bridge the gap between scientific evidence and legal requirements in animal welfare cases.

While originally designed for different purposes, the framework’s systematic approach to quantifying animal welfare experiences makes it a valuable tool for demonstrating suffering in legal settings. The interest from agencies like SENASA and OIJ suggests promising potential for its application in animal cruelty cases.

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