Is it better to be a wild rat or a factory farmed cow? A systematic method for comparing animal welfare.
TLDR: We looked at a lot of different systems to compare welfare and ended up combining a few common ones into a weighted animal welfare index (or welfare points for short). We think this system captures a broad range of ethical considerations and should be applicable across a wide range of both farm and wild animals in a way that allows us to compare interventions.
The goal of Charity Entrepreneurship is to compare different charitable interventions and actions so that new, strong charities can be founded. One of the necessary steps in such a process is having a way to compare different animals in different conditions. For example, how does moving a chicken from a battery cage to cage free compare in terms of the chicken's welfare? Or how does giving up red meat, thus resulting in one fewer cow brought into existence, compare to an insect dying more humanely because of a change in which insecticide is used? These are complex questions, surrounded by both ethical and epistemic uncertainty.
In the health community, disability-adjusted life years (DALYs) have become fairly common and established as a metric. Sadly, the same level of consensus does not exist within the animal rights community. We expected there would be multiple competing systems, so we first outlined what we would look for within a system to assess its helpfulness to us. This could be described as the purpose of the metric. Of course, the overarching goal is to help us evaluate different possible actions, but we broke down what we were looking for more specifically in the criteria below.
Underlying goals of metrics
We first looked within the EA community, since there had been some solid attempts at quantification. The below are a few of many examples.
Within the EA community
The next set of metrics we looked at was biology-based markers. We had some background knowledge of cortisol readings as a measure of stress. We hoped to find other objective markers that could make up part of a more inclusive system and add some objectivity to other soft systems. Some of the ones we considered (although there are many other possible biological indicators) are listed below.
Academic measures of quality
The third type of system we considered was academic measures of quality of life. WAS research had a great summary of many of the different systems used, but we also looked outside of their research for other possible systems.
Overall, we took a large number of elements of our system from the five domains model, which felt like the most extensively quantified and broad one of these models.
Systems used in global poverty
Next, we considered the current systems used in global poverty alleviation and other cause assessment areas. We thought it might be possible to modify one of these metrics to be usefully applicable to animals.
Modified poverty based metrics
Creating our own system
Finally, we considered creating a cross-applicable system from scratch
Our own ideas for possible systems
Results: an inclusive index
We ended up putting many of these systems onto a spreadsheet and comparing them on the original metric criteria we had derived. Some criteria ended up getting narrowed down. For example, we combined various biological markers into a single “biological markers” category. Some criteria were made more numerical and cross-comparable, for example, by translating the 5 domains model into number-based scores, instead of grades. Other elements were given their own category and weighting based on how well they met the top line criteria (for example, death rate). Most criteria were ruled out as redundant or not helpful for our purposes.
We ended up with 8 criteria with an importance weighting for each. Combined, they added to a range of +100 (an ideal life) to -100 (a perfectly unideal life) with 0 representing uncertainty about the life being net positive or negative. Each area can have positive or negative welfare scores and is to be rated independently, giving a more robust cluster approach to the overall endline score. The weighting of each factor is different, depending on how well it scored on our original metric criteria. For example, death rate gets a relatively higher weighting (20 welfare points) than our index of other biological markers (4 welfare points) due to its ease to work with and its clearer relation to direct animal suffering (e.g. we are more confident that animals with very high and painful death rates will correlate more strongly with a life not worth living than the more abstract biological markers will).
Factors we ended up using:
Overall, we felt like this system gave us a good balance between both the more subjective metrics that could capture more data and the harder metrics that were more objective. We feel that this system could be used across a wide range of both animals and interventions, and lead to cross-comparable results.