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Mitigating Evidentiary Bias in Planning and Policy-Making Comment on "Reflective Practice: How the World Bank Explored Its Own Biases?"

ABSTRACT: The field of cognitive psychology has increasingly provided scientific insights to explore how humans are subject to unconscious sources of evidentiary bias, leading to errors that can affect judgement and decision-making. Increasingly these insights are being applied outside the realm of individual decision-making to the collective arena of policy-making as well. A recent editorial in this journal has particularly lauded the work of the World Bank for undertaking an open and critical reflection on sources of unconscious bias in its own expert staff that could undermine achievement of its key goals. The World Bank case indeed serves as a remarkable case of a global policy-making agency making its own critical reflections transparent for all to see. Yet the recognition that humans are prone to cognitive errors has been known for centuries, and the scientific exploration of such biases provided by cognitive psychology is now well-established. What still remains to be developed, however, is a widespread body of work that can inform efforts to institutionalise strategies to mitigate the multiple sources and forms of evidentiary bias arising within administrative and policy-making environments. Addressing this gap will require a programme of conceptual and empirical work that supports robust development and evaluation of institutional bias mitigation strategies. The cognitive sciences provides a scientific basis on which to proceed, but a critical priority will now be the application of that science to improve policy-making within those agencies taking responsibility for social welfare and development programmes.

SUBMITTER: Parkhurst J

PROVIDER: S-EPMC5287925 | biostudies-other | 2016 Jul

REPOSITORIES: biostudies-other

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Project description:On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data. It is imperative and only ethical for modern scientists to be vigilant in inspecting and be prepared to mitigate the potential biases when working with any datasets. This article describes a framework to examine biases in scientific document collections like CORD-19 by comparing their properties with those derived from the citation behaviors of the entire scientific community. In total, three expanded sets are created for the analyses: 1) the enclosure set CORD-19E composed of CORD-19 articles and their references and citations, mirroring the methodology used in the renowned "A Century of Physics" analysis; 2) the full closure graph CORD-19C that recursively includes references starting with CORD-19; and 3) the inflection closure CORD-19I, that is, a much smaller subset of CORD-19C but already appropriate for statistical analysis based on the theory of the scale-free nature of the citation network. Taken together, all these expanded datasets show much smoother trends when used to analyze global COVID-19 research. The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary. A question-answering system with such expanded scope of knowledge may perform better in understanding the literature and answering related questions. However, while CORD-19 appears to have topical coverage biases compared to the expanded sets, the collaboration patterns, especially in terms of team sizes and geographical distributions, are captured very well already in CORD-19 as the raw statistics and trends agree with those from larger datasets.

Dataset Information

Mitigating Evidentiary Bias in Planning and Policy-Making Comment on "Reflective Practice: How the World Bank Explored Its Own Biases?"

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