How biological background assumptions influence scientific risk evaluation of stacked genetically modified plants: an analysis of research hypotheses and argumentations

Philosophical bias is the one bias that science cannot avoid

Scientists seek to eliminate all forms of bias from their research. However, all scientists also make assumptions of a non-empirical nature about topics such as causality, determinism and reductionism when conducting research. Here, we argue that since these 'philosophical biases' cannot be avoided, they need to be debated critically by scientists and philosophers of science.

From Ideal to Real Risk: Philosophy of Causation Meets Risk Analysis

A question has been raised in recent years as to whether the risk field, including analysis, assessment, and management, ought to be considered a discipline on its own. As suggested by Terje Aven, unification of the risk field would require a common understanding of basic concepts, such as risk and probability; hence, more discussion is needed of what he calls "foundational issues." In this article, we show that causation is a foundational issue of risk, and that a proper understanding of it is crucial. We propose that some old ideas about the nature of causation must be abandoned in order to overcome certain persisting challenges facing risk experts over the last decade. In particular, we discuss the challenge of including causally relevant knowledge from the local context when studying risk. Although it is uncontroversial that the receptor plays an important role for risk evaluations, we show how the implementation of receptor-based frameworks is hindered by methodological shortcomings that can be traced back to Humean orthodoxies about causation. We argue that the first step toward the development of frameworks better suited to make realistic risk predictions is to reconceptualize causation, by examining a philosophical alternative to the Humean understanding. In this article, we show how our preferred account, causal dispositionalism, offers a different perspective in how risk is evaluated and understood.

The judgements that evidence-based medicine adopts

In "The evidence that evidence-based medicine omits", Brendan Clarke and colleagues argue that when establishing causal facts in medicine, evidence of mechanisms ought to be included alongside evidence of correlations. One of the reasons they provide is that correlations can be spurious and generated by unknown confounding variables. A causal mechanism can provide a plausible explanation for the correlation, and the absence of such an explanation is an indication that the correlation is not causal. Evidence-based medicine (EBM) proponents remain sceptical about this argument, one problem being that the formulation of a mechanism requires judgements that are external to the evaluation of data and experimental designs-for instance judgements of plausibility against, or derivability from, background knowledge. Because background knowledge is always incomplete and therefore unreliable, EBM proponents maintain that the plausibility of a hypothesis should be evaluated mainly by the quality of population data that yielded it. Here, I use the example of oestrogen replacement therapy's effect on coronary heart disease, an example that is often quoted in defence of the epistemic advantage of randomized controlled trials, to show that the evaluation of the most reliable study design necessarily implies the adoption of judgements that are external to the specific evidence of correlation. The exclusion of evidence of mechanism, therefore, is not effective in bypassing paradigm-dependent judgements, which are external to specific evidence. Because such judgements cannot be excluded by evidence evaluation, they can only be kept under scrutiny, or adopted uncritically. I propose that the latter option can hinder the maintenance of an active critical inquiry, as well as the analysis of experts' disagreement.