Scientific risk evaluations are constructed by specific evidence, value judgements and biological background assumptions. The latter are the framework-setting suppositions we apply in order to understand some new phenomenon. That background assumptions co-determine choice of methodology, data interpretation, and choice of relevant evidence is an uncontroversial claim in modern basic science. Furthermore, it is commonly accepted that, unless explicated, disagreements in background assumptions can lead to misunderstanding as well as miscommunication. Here, we extend the discussion on background assumptions from basic (...) science to the debate over genetically modified (GM) plants risk assessment. In this realm, while the different political, social and economic values are often mentioned, the identity and role of background assumptions at play are rarely examined. We use an example from the debate over risk assessment of stacked genetically modified plants (GM stacks), obtained by applying conventional breeding techniques to GM plants. There are two main regulatory practices of GM stacks: (i) regulate as conventional hybrids and (ii) regulate as new GM plants. We analyzed eight papers representative of these positions and found that, in all cases, additional premises are needed to reach the stated conclusions. We suggest that these premises play the role of biological background assumptions and argue that the most effective way toward a unified framework for risk analysis and regulation of GM stacks is by explicating and examining the biological background assumptions of each position. Once explicated, it is possible to either evaluate which background assumptions best reflect contemporary biological knowledge, or to apply Douglas' 'inductive risk' argument. (shrink)

ABSTRACTMeasurements of environmental toxicity from long-term exposure to oil contamination have delivered inaccurate and contradictory results regarding the potential harms for humans and ecosyste...

Scholars in science and technology studies, as well as economics and innovation studies, utilize the trajectory metaphor in describing a technology’s maturation. Impetus and purpose may differ, but the trajectory serves as a shared tool for assessing social change either in society at large or within a market sector, a firm, or a discipline. In reverse, the lens of a technology trajectory can be a basis for assessing technology, estimating economic growth, and selecting among plausible product development pathways. Emerging technologies (...) pose a challenge in that the promissory claims that attracted investment may also challenge social safeguards, leading to necessary, but unforeseen hurdles to later technology acceptance. The experiences with biotechnology and nanotechnology demonstrate that knowledge gained in the intervening years was to have tempered exaggerations and addressed the criteria and the responsibilities of those who would later be deciding on product acceptability. In particular, the promissory claim for nanotechnology, unique phenomena, challenged regulatory practice, while claims for biotechnology products were often the fruition of themes found in the educational experience common to both the biotechnology researcher and the regulator. The social science and humanities literature mirrored these points as did the responses of civil society organizations. A prospective trajectory for artificial intelligence is developed using the example of driverless cars and then qualitatively compared to the retrospective trajectories for nanotechnology and biotechnology. Experience with automation, algorithm transparency, and computational modeling of biological mechanisms are identified as pertinent to responsible AI development. (shrink)

. The potential for financial conflicts of interest to influence scientific research has become a significant concern. Some commentators have suggested that the development of standardized study protocols could help to alleviate these problems. This paper identifies two problems with this solution: scientific research incorporates numerous methodological judgments that cannot be constrained by standardized protocols; and standardization can hide significant value judgments. These problems arise because of four weaknesses of standardized guidelines: incompleteness, limited applicability, selective ignorance, and ossification. Therefore, the (...) standardization of study designs should not serve as an alternative to addressing the interests and power relations that pervade science policy-making. Policy makers should take at least two steps to prevent powerful interest groups from co-opting standardized guidelines. First, their development and review should be made as transparent as possible and should be subjected to broadly-based deliberation. Second, standardized guidelines should be supplemented with efforts to scrutinize the conditions under which financial COIs tend to have the most worrisome effects so that additional steps can be taken to eliminate and mitigate those conditions. (shrink)

Both scientists and philosophers of science have recently emphasized the importance of promoting transparency in science. For scientists, transparency is a way to promote reproducibility, progress, and trust in research. For philosophers of science, transparency can help address the value-ladenness of scientific research in a responsible way. Nevertheless, the concept of transparency is a complex one. Scientists can be transparent about many different things, for many different reasons, on behalf of many different stakeholders. This paper proposes a taxonomy that clarifies (...) the major dimensions along which approaches to transparency can vary. By doing so, it provides several insights that philosophers and other science studies scholars can pursue. In particular, it helps address common objections to pursuing transparency in science, it clarifies major forms of transparency, and it suggests avenues for further research on this topic. (shrink)

In recent years, industry-funded research has come under fire because of concerns that it can be biased in favor of the funders. This article suggests that efforts by philosophers of science to analyze the concept of objectivity can provide important lessons for those seeking to evaluate and improve industry-funded research. It identifies three particularly relevant criteria for objectivity: transparency, reproducibility, and effective criticism. On closer examination, the criteria of transparency and reproducibility turn out to have significant limitations in this context, (...) but the criterion of effective criticism suggests some important strategies for improving the objectivity of industry-funded science. (shrink)