Hi Vasco, thanks for this follow up. I believe we cannot rule out the possibilities you mentioned. That is, the answer could be yes to both questions. Even with humans, there is high variability in preference for intense but short aversiveness, as compared to moderate but longer aversion. 

One solution we have been adopting lately is to simply 'add up' times in the different categories (with no weighting or conversion). Since the 'boundaries between intensity categories' are acknowledged to be uncertain (i.e., we assign probabilities to each intensity category), adding up the time spent in different intensities follows naturally from this uncertainty. For instance, hours in Hurtful, Disabling, and Excruciating pain can be added together to report total time in 'moderate to intense pain,' or hours in Disabling, and Excruciating pain can be added together to report total time in 'intense pain'. 

Hi Vasco, that's a good question, but I do not think we can say with certainty that the ratio is preserved across species. The ratio may differ across species for a number of reasons, such as species-specific differences in the subjective perception of time. In fact, the relationship between the aversiveness of pain and its intensity may itself change dynamically for a 'same' species, depending on the duration of the experience. If on the one hand it's true that the 'criteria' defining each intensity category is universal (the same for all species), we cannot know for sure whether Excruciating pain in a shrimp is the same as Excruciating pain in a human being.
 

Thank you for this thought-provoking article, Vasco! In situations where we have clear, certain benefits in one area (here, knowing that use of slower-growing chicken breeds clearly reduces suffering for those chickens, even if their lives are longer) and a high degree of uncertainty on multiple dimensions in another (here the welfare consequences for arthropods), my inclination is that it's often more effective to focus on the suffering we are confident we can alleviate, and avoid  the non-negligible risk of relevant unintended negative consequences in the uncertain case (while simultaneously promoting further research on the latter effects). 

Hi CB, thanks a lot for your comment, I think it represents a main concern of many people. I'll break my thoughts in two parts

(1) AI use in shrimp farming and similar situations.

In this case, I understand what AI-monitoring did was to enable farmers to optimize feed use enormously (shrimp grew larger, mortality was reduced, and feed was not wasted), as well as water quality monitoring. This could be seen as negative for welfare, as it facilitates farming in high stocking densities, makes shrimp farming more profitable and could reduce prices, though this price effect is complex since the same AI technologies will likely make alternative proteins cheaper too, making the net effect on consumption less certain.

However, consider the actual conditions shrimp face. Without AI, feed distribution was uneven, leading to competition, stress, malnutrition and starvation for a large fraction of animals (mortality without AI was higher), as well as longer exposure times to poor water quality, and higher incidence of toxicities (hence respiratory distress, gill damage, skin damage) that come associated with it. This leads to suffering and higher mortality rates. So it's possible (though this should be measured) that even in higher-density environments, AI use can maintain better welfare than lower-density farms with poor feed and water quality management. Importantly , if shrimp feed relies on fishmeal and fish oil, optimizing feed reduces the number of wild fish needed, so each pound of shrimp has a smaller welfare footprint in terms of wild fish captures.

The industry trajectory also matters. Aquaculture is already moving toward higher-density and intensified farming with or without AI. So I believe the relevant comparison isn't between AI farming and a low-density or extensive scenario, but between AI-farming and conventional (intensive) high-density farming without AI. 

(2) On AI leading to greater income/prosperity, potentially increasing consumption of animal foods.

I see greater incomes and prosperity as extremely positive to reduce human suffering, but animal suffering as well. While rising incomes historically increased meat consumption, the relationship is not linear, in that as societies become more prosperous (on top of being an extraordinary thing in itself), they often can afford being more concerned with environmental and ethical issues. It's particularly in wealthier nations that we see a trend towards reduced meat consumption, stronger welfare legislation, increased interest in plant-based alternatives, and the means needed for the development of innovations like cultivated meat and other substitutes of animal protein. And again, the same technologies making animal farming more efficient are simultaneously making alternatives more competitive and affordable. I believe that the key isn't if AI increases income (something to be celebrated), but how to channel greater incomes toward ethical food systems.

Hi Vasco! Yes, it would be very interesting to collaborate on this. Right now we do not have the resources (in terms of people, and time) to do it ourselves, but we would gladly collaborate with anyone leading this effort. One possibility would be running WTP tests with people, from various demographics, to determine the extent to which they would pay to trade Disabling Pain by Hurtful Pain, or Excruciating Pain by Disabling Pain and so on (having the understanding of these intensities well explained and calibrated with examples, past experiences, etc, within a clear set of criteria).  This would help understand some level of equivalence (though from a "human" perspective) between the categories, but also generate rational WTP numbers for any estimates of Cumulative Pain (e.g., if cage-free campaigns avert X hours of Disabling Pain per hen, and people are willing to pay on average 1-10 dollars to avert one hour of this pain, you can in theory calculate the extent to which they would pay more for improved welfare, run CBA analysis, apply these to funding decisions, etc)

By not tolerated we mean that Excruciating pain can't be sustained for long (e.g., hours, as opposed to minutes) without neurological shutdown. It will tend to overrides an organism's ability to function or respond coherently, with even powerful opioids providing minimal relief, or desperate attempts to escape pain even at the risk of death . Examples would include severe burning in large areas of the body, dismemberment, or extreme torture. 
Examiing empirically trade-offs with milder pain could be interesting, but may be challenging given the nature of truly excruciating pain . Perhaps comparing to severe but more sustained pain (like bone fractures)?
 

This is indeed a legitimate concern. We do not have accurate information on the distribution of BCC-approved breeds used in the committments made so far, but I believe that organizations working on and monitoring the committments (possibly the Humane League and CIWF, which publishes the Chicken Track), are likely to have this information. From statements of company's representatives, it seems that the Hubbard breeds are prevailing in Europe, see e.g. this statement: "In Europe, where the issue of breed is more advanced than in the U.S., the Hubbard JA757, JA787 and Redbro are the main breeds used for the BCC market".

It may be interesting to know though that an experiment published recently, which used Avian Ranger Classic (the fastest growing of the Aviagen breeds), consistently showed various welfare benefits associated with this slower-growing breed, even when it was raised in higher stocking densities than the conventional fast-growing breeds (showing that it is better to be this slower-growing breed in high densities than a fast-growing breed in low densities).  

If also useful: our estimates are very conservative, and represent what we estimate to be the minimum time in pain averted with the reform. In this chapter (table 1), there is a list of welfare harms excluded from analysis, which if considered should increase further the estimated benefits of the transition to  slower growing breeds.

Thanks! I will revise the post to make sure it is clearer!

This is an interesting question, relating to the evolutionary role of pain going beyond protection of the individual survival and immediate reproduction (or that of kin), but affecting the group as a whole. As you said, debilitating pain (i.e., pain that prevents individuals to function normally) for a solitary species may also have different moral implications than for a social species even if the unpleasantness of the pain experience is the same.

My impression is that it would be difficult to determine differences in pain aversiveness among species with different degrees of sociality given the confounders present. For example, there might be differences in hedonic and cognitive capacity between social and non-social species (see eg here), hence differences in aversiveness to pain for reasons other than the context of the painful experience.

Thank you for the detailed comments, it is really nice to see you were so thorough with the text and the studies we cite, these are good points!

If we understand you well, with a few exceptions (as in the case of the time trade-off study, [21]), what you mention is that the superlinearity observed could be a by-product of participants of the studies interpreting the designed scales of intensity (even the numerical ones) as not equidistant, hence a non-equidistant plot of aversiveness could, for example, be observed if the relationship between intensity and aversiveness was in fact linear.

My impression, however, is that we are referring to different things when we say pain intensity (and this is our fault for not being clearer on the text, we will make the distinction clearer). My understanding is that ‘pain intensity’ is often used simply as a synonym for aversiveness or unpleasantness, as opposed to physical intensity of stimuli/pain signals. 

As I see it, assessments of pain intensity (unpleasantness) are typically made on what can only be understood as an ordinal scale. Whether evidence on pain unpleasantness is collected with verbal descriptors or numerical descriptors, these unpleasantness scales (i.e. the intensity scales used) can only be interpreted as ordinal exactly because we do not know what type of understanding of the scale study participants have. Even though some authors use the data collected (e.g., pain intensity/unpleasantness ratings in scales from 1-10 or 1-100) and calculate things such as areas under the curve for plots of ‘intensity’ (meant as unpleasantness) ratings x duration, the data is still ordinal for practical purposes and unsuitable for these operations. 

So the effort in this work was to try and see if it would be possible to make such a conversion of pain unpleasantness (from ordinal to ratio scale), and determine the distance among the categories of unpleasantness on a ratio scale. Thus, in the case of the psychophysics studies, I do not see three parameters: intensity of physical stimuli, pain intensity and pain aversiveness, but only two: intensity of physical stimuli and aversiveness (we will correct the text where we mention that the relationship is exponential).

On the availability of studies, unfortunately, all of them were riddled with limitations. Although designs such as the time trade-off approach (used in study [21]) are much better, to our surprise the literature is extremely scarce. So no good studies, and no recent studies either, as far as we are aware. This is why we currently prefer a disaggregated approach, as we do not see how, with the evidence at hand, it is possible to estimate the equivalence weights with any precision .