Both the cases discussed above are classic examples of scientists self-identifying as “objective” and utterly failing to recognize they are human and, thus, fallible.
40 Both Freeman and Hudlický wandered into areas of scholarship where they had no acquired or demonstrated expertise and presented uninformed opinion with no apparent self-awareness. These case studies highlight the importance, especially for the dominant demographic
2,3,5,7 of scientists, to consciously and intentionally apply the same rigour to observation of themselves and the chemistry culture as they do to the practice of chemistry. Expertise, by definition, reflects knowledge of a specific domain: being an expert in organic synthesis, physical chemistry, polymer chemistry, etc., does not inherently make one an expert in anything else, particularly the social sciences. However, we can use scientific approaches to understanding ourselves (hence the value of reference to and engagement with social science research and scholars). We can, and should, analyze our own behaviours to identify and correct for bias, just as we would identify a machine in the lab or a step in a process that is inefficient or mis-calibrated, and correct for it to ensure that our outputs are correct. We illustrate this process by using an analogy in
Fig. 1. If we consider the series of actions that need to be undertaken to identify a specific molecule of interest from a mixture of proteins, the usage of mis-calibrated equipment will inevitably result in inaccurate data. If we replace “molecule of interest” with “excellence” in
Fig. 1, we can see that many of the processes currently used to determine “excellence” are demonstrably flawed (e.g., evaluation of the CV, committee evaluations, assessments of quality of contributions) due to mis-calibrations, which, when repeated at various stages in a process, are inevitably amplified. Ultimately, the faulty process will lead to significant errors in outputs (e.g., selection towards homogeneity, subjective success rates of grants or awards). Computer simulations of repeated rounds of bias show that over-representation of one demographic relative to another of up to 20% can occur.
41,42 Scientists pride themselves on their objectivity but, being human, are really one more component in a system that requires attentive calibration, similarly to what is applied to our laboratory equipment. We must continually calibrate ourselves in terms of our biases to ensure that we are assessing our science and our scientific community as rigorously and accurately as possible. This can be done through education and awareness raising, implicit bias training, and many other approaches that have been well described. We should also be intentional about ensuring the inclusion of talent from across the spectrum of humanity (
Fig. 2). Humility and the ability to acknowledge error are hallmarks of great scientists, as described by Seebach himself whom, in his concluding remarks (which expressed the hope that his piece had not overpromised) quoted Teresa of Avila (1515–1582) “Teach me the glorious lesson that occasionally it is possible that I may be mistaken”.
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