EA - Counterproductive Altruism: The Other Heavy Tail by Vasco Grilo

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Counterproductive Altruism: The Other Heavy Tail, published by Vasco Grilo on March 1, 2023 on The Effective Altruism Forum.This is a linkpost to the article Counterproductive Altruism: The Other Heavy Tail from Daniel Kokotajlo and Alexandra Oprea. Some excerpts are below. I also include a section at the end with some hot takes regarding possibly counterproductive altruism.AbstractFirst, we argue that the appeal of effective altruism (henceforth, EA) depends significantly on a certain empirical premise we call the Heavy Tail Hypothesis (HTH), which characterizes the probability distribution of opportunities for doing good. Roughly, the HTH implies that the best causes, interventions, or charities produce orders of magnitude greater good than the average ones, constituting a substantial portion of the total amount of good caused by altruistic interventions. Next, we canvass arguments EAs have given for the existence of a positive (or “right”) heavy tail and argue that they can also apply in support of a negative (or “left”) heavy tail where counterproductive interventions do orders of magnitude more harm than ineffective or moderately harmful ones. Incorporating the other heavy tail of the distribution has important implications for the core activities of EA: effectiveness research, cause prioritization, and the assessment of altruistic interventions.It also informs the debate surrounding the institutional critique of EA.IV Implications of the Heavy Right Tail for AltruismAssume that the probability distribution of charitable interventions has a heavy-right tail (for example, like the power law described in the previous section). This means that your expectation about a possible new or unassessed charitable intervention should include the large values described above with a relatively high probability. It also means that existing charitable interventions whose effectiveness is known (or estimated with a high degree of certainty) will include interventions differing in effectiveness by orders of magnitude. We contend that this assumption justifies well-known aspects of EA practice such as (1) effectiveness research and cause prioritization, (2) “hits-based-giving,” and (3) skepticism about historical averages.V Implications of the Heavy Left Tail for AltruismWhat if the probability distribution of altruistic interventions includes both a left and a right heavy tail? In this case, we cannot assume either that (1) one's altruistic interventions are expected to have at worst a value of zero (i.e. to be bounded on the left side) or (2) that the probability that a charitable intervention is counterproductive or harmful approaches zero very rapidly.Downside Risk ResearchMany catastrophic interventions — whether altruistic or not — generate large amounts of (intentional or unintentional) harm. When someone in the world is engaging in an intervention that is likely to end up in the heavy left tail, there is a corresponding opportunity for us to do good by preventing them. This would itself represent an altruistic intervention in the heavy right tail (i.e. one responsible for enormous benefits). The existence of the heavy-left tail therefore provides even stronger justification for the prioritization research preferred by EAs.Assessing Types of Interventions Requires Both TailsAnother conclusion we draw from the revised HTH is that the value of a class of interventions should be estimated by considering the worst as well as the best. Following such analysis, a class of interventions could turn out to be net-negative even if there are some very prominent positive examples and indeed even if almost all examples are positive. This sharply contradicts MacAskill's earlier claim that the value of a class of interventions can be approximated by the value of its best membe...