Recognizing that truth is socially constructed or that knowledge and power are related is hardly a novelty in the social sciences. In the twenty-first century, however, there appears to be a renewed concern regarding people's relationship with the truth and the propensity for certain actors to undermine it. Organizations are highly implicated in this, given their central roles in knowledge management and production and their attempts to learn, although the entanglement of these epistemological issues with business ethics has not been engaged as explicitly as it might be. Drawing on work from a virtue epistemology perspective, this paper outlines the idea of a set of epistemic vices permeating organizations, along with examples of unethical epistemic conduct by organizational actors. While existing organizational research has examined various epistemic virtues that make people and organizations effective and responsible epistemic agents, much less is known about the epistemic vices that make them ineffective and irresponsible ones. Accordingly, this paper introduces vice epistemology, a nascent but growing subfield of virtue epistemology which, to the best of our knowledge, has yet to be explicitly developed in terms of business ethics. The paper concludes by outlining a business ethics research agenda on epistemic vice, with implications for responding to epistemic vices and their illegitimacy in practice.

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Recognizing that truth is socially constructed or that knowledge and power are related is hardly a novelty in the social sciences. In the twenty-first century, however, there appears to be a renewed concern regarding people's relationship with the truth and the propensity for certain actors to undermine it. Organizations are highly implicated in this, given their central roles in knowledge management and production and their attempts to learn, although the entanglement of these epistemological issues with business ethics has not been engaged as explicitly as it might be. Drawing on work from a virtue epistemology perspective, this paper outlines the idea of a set of epistemic vices permeating organizations, along with examples of unethical epistemic conduct by organizational actors. While existing organizational research has examined various epistemic virtues that make people and organizations effective and responsible epistemic agents, much less is known about the epistemic vices that make them ineffective and irresponsible ones. Accordingly, this paper introduces vice epistemology, a nascent but growing subfield of virtue epistemology which, to the best of our knowledge, has yet to be explicitly developed in terms of business ethics. The paper concludes by outlining a business ethics research agenda on epistemic vice, with implications for responding to epistemic vices and their illegitimacy in practice.

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,343 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,067 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM were more sensitive to the detection of significant case-control differences in regional cortical thickness (Chi2(3) = 34.339, p < 0.001), and case-control differences in age-related cortical thinning (Chi2(3) = 15.128, p = 0.002). Specifically, ComBat-GAM led to larger effect size estimates of cortical thickness reductions (corrected p-values < 0.001), smaller age-appropriate declines (corrected p-values < 0.001), and lower female to male contrast (corrected p-values < 0.001) in cases compared to controls relative to other harmonization methods. Harmonization with ComBat-GAM also led to greater estimates of age-related declines in cortical thickness (corrected p-values < 0.001) in both cases and controls compared to other harmonization methods. Our results support the use of ComBat-GAM for harmonizing cortical thickness data aggregated from multiple sites and scanners to minimize confounds and increase statistical power.

Background: Current clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: Our findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD.