Kate Lynch

The rapid rise of generative AI, including LLMs, has prompted a great deal of concern, both within and beyond academia. One of these concerns is that generative models embed, reproduce, and therein potentially perpetuate all manner of bias. The present study offers an alternative perspective: exploring the potential of LLMs to detect bias in human generated text. Our target is genetic essentialism in obesity discourse in Australian print media. We develop and deploy an LLM-based classification model to evaluate a large sample of relevant articles (n = ∼26,000). We show that our model detects genetic essentialist biases as reliably as human experts; and find that, while genes figure less prominently in popular discussions of obesity than previous work might suggest, when genetic information is invoked, it is often presented in a biased way. Implications for future work are discussed.

The aim of this virtual special issue is to bring together philosophical and historical perspectives to address long-standing issues in the interpretation, utility, and impacts of quantitative genetics methods and findings. Methodological approaches and the underlying scientific understanding of genetics and heredity have transformed since the field's inception. These advances have brought with them new philosophical issues regarding the interpretation and understanding of quantitative genetic results. The contributions in this issue demonstrate that there is still work to be done integrating old and new methodological and conceptual frameworks. In some cases, new results are interpreted using assumptions based on old concepts and methodologies that need to be explicitly recognised and updated. In other cases, new philosophical tools can be employed to synthesise historical quantitative genetics work with modern methodologies and findings. This introductory article surveys three general themes that have dominated philosophical discussion of quantitative genetics throughout history: (1) how methodologies have changed and transformed our knowledge and interpretations; (2) whether or not quantitative genetics can offer explanations relating to causation and prediction; and (3) the importance of defining the phenotypes under study. We situate the contributions in this virtual special issue within a historical framework addressing these three themes.

Causation has multiple distinct meanings in genetics. One reason for this is meaning slippage between two concepts of the gene: Mendelian and molecular. Another reason is that a variety of genetic methods address different kinds of causal relationships. Some genetic studies address causes of traits in individuals, which can only be assessed when single genes follow predictable inheritance patterns that reliably cause a trait. A second sense concerns the causes of trait differences within a population. Whereas some single genes can be said to cause population-level differences, most often these claims concern the effects of many genes. Polygenic traits can be understood using heritability estimates, which estimate the relative influences of genetic and environmental differences to trait differences within a population. Attempts to understand the molecular mechanisms underlying polygenic traits have been developed, although causal inference based on these results remains controversial. Genetic variation has also recently been leveraged as a randomizing factor to identify environmental causes of trait differences. This technique—Mendelian randomization—offers some solutions to traditional epidemiological challenges, although it is limited to the study of environments with known genetic influences.

When explaining the causes of human behavior, genes are often given a special status. They are thought to relate to an intrinsic human 'essence', and essentialist biases have been shown to skew the way in which causation is assessed. Causal reasoning in general is subject to other pre-existing biases, including beliefs about normativity and morality. In this synthesis we show how factors which influence causal reasoning can be mapped to a framework of genetic essentialism, which reveals both the shared and unique factors underpinning biases in causal reasoning and genetic essentialism. This comparison identifies overlooked areas of research which could provide fruitful investigation, such as whether normative assessments of behaviors influence the way that genetic causes are ascribed or endorsed. We also outline the importance of distinguishing reasoning processes regarding genetic causal influences on one's self versus others, as different cognitive processes and biases are likely to be at play.