142 Open Science: Improving Access and Reducing Bias in Science

The promise of science lies in the discovery of basic knowledge, new treatments for disease and possible solutions to the world’s problems. Fulfilling this promise requires confidence that the findings of published science are valid—that they represent an unbiased conclusion based on available data. In recent years, however, a “reproducibility crisis” has emerged indicating that published findings across research fields may be less credible than they seem, perhaps due to hidden biases in the research process. This talk will provide an overview of the key challenges that reduce the credibility and reproducibility of research and will discuss how open science practices address these challenges. Current practice is sustained by a dysfunctional incentive structure that prioritizes publication over accuracy. Changing the research culture to prioritize “getting it right” over “getting it published” requires nudges to the incentive landscape, while still fueling the engine of innovation and discovery that drives science into new domains.

Automated geological map deconstruction for 3D model construction

We present two Python libraries (map2loop and map2model) which combine the observations available in digital geological maps with conceptual information, including assumptions regarding the subsurface extent of faults and plutons to provide sufficient constraints to build a reasonable 3D geological model. At a regional scale, the best predictor for the 3D geology of the near-subsurface is often the information contained in a geological map. This remains true even after recognising that a map is also a model, with all the potential for hidden biases that this model status implies. One challenge we face is the difficulty in reproducibly preparing input data for 3D geological models. The information stored in a map falls into three categories of geometric data: positional data such as the position of faults, intrusive and stratigraphic contacts; gradient data, such as the dips of contacts or faults and topological data, such as the age relationships of faults and stratigraphic units, or their adjacency relationships. This work is being conducted within the Loop Consortium, in which algorithms are being developed that allow automatic deconstruction of a geological map to recover the necessary positional, gradient and topological data as inputs to different 3D geological modelling codes. This automation provides significant advantages: it reduces the time to first prototype models; it clearly separates the primary data from subsets produced from filtering via data reduction and conceptual constraints; and provides a homogenous pathway to sensitivity analysis, uncertainty quantification and Value of Information studies. We use the example of the re-folded and faulted Hamersley Basin in Western Australia to demonstrate a complete workflow from data extraction to 3D modelling using two different Open Source 3D modelling engines: GemPy and LoopStructural.

Defining Health Disparities in Terms of Equity

Disparities in healthcare limit accessibility to care among affected populations and can include imbalances in the equitable achievement of optimal health. These imbalances occur as a result of differences that others have in financial means, education, culture, age, race, gender, sex, ethnicity, and religion. Consequentially, as health disparities persist among populations, mortality and morbidity rates reflect these inequities in health care. Hence, human life is quantified by geographic location, skin color, language, poverty, and an inability to culturally assimilate with majority populations. Hidden biases overshadow the pricelessness of human life, disease management, and disease prevention. Chapter 1 provides an overview of what encompasses health disparities and how equity is involved. Vulnerable populations within the United States are examined, and hidden biases are discussed as factors that impact the achievement of equitable healthcare.

Crouching Beliefs, Hidden Biases

The debate among economists about an optimal growth recipe has been the subject of competing “narratives.” We identify four major growth narratives using the text analytics of IMF country reports over 1978-2019. The narrative “Economic Structure”—services, manufacturing, and agriculture—has been on a secular decline overshadowed by the “Structural Reforms”—competitiveness, transparency, and governance. We observe the rise and fall of the “Washington Consensus”—privatization and liberalization— and the rise to dominance of the “Washington Constellation,” a collection of many disparate terms such as productivity, tourism, and inequality. Growth theory concepts such as innovation, technology, and export policy have been marginal while industrial policy, which was once perceived positively, is making a comeback.

Infinite Lotteries, Spinners, Applicability of Hyperreals†

We analyze recent criticisms of the use of hyperreal probabilities as expressed by Pruss, Easwaran, Parker, and Williamson. We show that the alleged arbitrariness of hyperreal fields can be avoided by working in the Kanovei–Shelah model or in saturated models. We argue that some of the objections to hyperreal probabilities arise from hidden biases that favor Archimedean models. We discuss the advantage of the hyperreals over transferless fields with infinitesimals. In Paper II we analyze two underdetermination theorems by Pruss and show that they hinge upon parasitic external hyperreal-valued measures, whereas internal hyperfinite measures are not underdetermined.

MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care

The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present MINIMAR (MINimum Information for Medical AI Reporting), a proposal describing the minimum information necessary to understand intended predictions, target populations, and hidden biases, and the ability to generalize these emerging technologies. We call for a standard to accurately and responsibly report on AI in health care. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias.

Ideological measurement in social and political psychology

As social psychology undergoes marked changes in its approach to research (e.g., open science practices, and addressing the replication crisis), it is important to undertake a full review of the tools and measures that we have at our disposal. In addition, the growing sense of political and ideological polarization in contemporary western democracies necessitates a coherent and internally consistent approach to studying politically and ideologically sensitive topics. This paper explores the measurement and study of such topics, and posits that claims about (a)symmetries between ideological partisans may be rooted in different measurement approaches. That is, studies (and researchers) who report widespread differences (asymmetries) between partisan liberals and conservatives typically adopt individual difference designs that examine trait-level constructs. In contrast, those who typically report similarities (symmetries) between these groups collect situationally derived data. A more consistent and ecologically valid approach to studying partisan engagement with political topics is advocated, focusing on situational responses to ideologically salient scenarios, rather than placing our focus on results from decontextualized self-report individual difference measures. Underpinning this review are three key assertions. First, that our ideological homogeneity as a field blinds us to hidden biases in our methods. Second, that the aforementioned (a)symmetry camps talk past each other by adopting different epistemological approaches. Third, that addressing these shortcomings can allow us to better conform to the Mertonian norms of communalism, universalism, disinterestedness, and organized skepticism. In doing so, we can revive our status as an open, accurate, and reproducible scientific field.

From the Bigot in Our Midst to Good People with Hidden Biases

This chapter examines two significant stages in the scientific study of prejudice and intergroup relations as a resource for understanding bigotry. It illustrates the first, the post–World War II period, with Gordon W. Allport’s foundational The Nature of Prejudice (1954). The chapter explores the tension in Allport’s work between viewing the bigot as a distinct personality type and viewing prejudice and stereotypes as the outgrowth of ordinary cognitive processes. It analyzes other relevant features of Allport: the social contact hypothesis; the argument that “stateways” (antidiscrimination law) could change “folkways” by enlisting conscience to fight prejudice; and religion’s role in fostering but also condemning bigotry. The chapter explains how social scientists measured prejudice through people’s attitudes toward intermarriage. The second stage the chapter evaluates is study of implicit (or hidden) bias and unconscious cognition (“the bigot in your brain”). Such study maintains that people can recognize and fight those biases.