Philosophers of science widely believe that the hereditarian theory about racial differences in IQ is based on methodological mistakes and confusions involving the concept of heritability. I argue that this "received view" is wrong: methodological criticisms popular among philosophers are seriously misconceived, and the discussion in philosophy of science about these matters is largely disconnected from the real, empirically complex issues debated in science.

The critics of "hereditarianism" often claim that any attempt to explain human behavior by invoking genes is confronted with insurmountable methodological difficulties. They reject the idea that heritability estimates could lead to genetic explanations by pointing out that these estimates are strictly valid only for a given population and that they are exposed to the irremovable confounding effects of genotype-environment interaction and genotype-environment correlation. I argue that these difficulties are greatly exaggerated, and that we would be wrong to regard them (...) as presenting a fundamental obstacle to the search for genetic explanations. I also show that, to the extent they are cogent, these objections may prove to be even more damaging to the "environmentalist" standpoint. (shrink)

The method in human genetics of ascribing causal responsibility to genotype by the use of heritability estimates has been heavily criticized over the years. It has been argued that these estimates are rarely valid and do not serve the purpose of tracing genetic causes. Recent contributions strike back at this criticism. I present and discuss two opposing views on these matters represented by Richard Lewontin and Neven Sesardic, and I suggest that some of the disagreement is based on differing concepts (...) of genetic causation. I use the distinction of structuring and triggering causes to help clarifying the basis for the opposing views. (shrink)

The ML community recognizes the importance of anticipating and mitigating the potential negative impacts of benchmark research. In this position paper, we argue that more attention needs to be paid to areas of ethical risk that lie at the technical and scientific core of ML benchmarks. We identify overlooked structural similarities between human IQ and ML benchmarks. Human intelligence and ML benchmarks share similarities in setting standards for describing, evaluating and comparing performance on tasks relevant to intelligence. This enables us (...) to unlock lessons from feminist philosophy of science scholarship that need to be considered by the ML benchmark community. Finally, we outline practical recommendations for benchmark research ethics and ethics review. (shrink)