Responsible data sharing: Identifying and remedying possible re-identification of human participants.

Open data collected from research participants creates a tension between scholarly values of transparency and sharing, on the one hand, and privacy and security, on the other hand. A common solution is to make data sets anonymous by removing personally identifying information (e.g., names or worker IDs) before sharing. However, ostensibly anonymized data sets may be at risk of re-identification if they include demographic information. In the present article, we provide researchers with broadly applicable guidance and tangible tools so that they can engage in open science practices without jeopardizing participants' privacy. Specifically, we (a) review current privacy standards, (b) describe computer science data protection frameworks and their adaptability to the social sciences, (c) provide practical guidance for assessing and addressing re-identification risk, (d) introduce two open-source algorithms developed for psychological scientists-MinBlur and MinBlurLite-to increase privacy while maintaining the integrity of open data, and (e) highlight aspects of ethical data sharing that require further attention. Ultimately, the risk of re-identification should not dissuade engagement with open science practices. Instead, technical innovations should be developed and harnessed so that science can be as open as possible to promote transparency and sharing and as closed as necessary to maintain privacy and security. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

All human social groups are human, but some are more human than others: A comprehensive investigation of the implicit association of "Human" to US racial/ethnic groups.

All human groups are equally human, but are they automatically represented as such? Harnessing data from 61,377 participants across 13 experiments (six primary and seven supplemental), a sharp dissociation between implicit and explicit measures emerged. Despite explicitly affirming the equal humanity of all racial/ethnic groups, White participants consistently associated Human (relative to Animal) more with White than Black, Hispanic, and Asian groups on Implicit Association Tests (IATs; experiments 1-4). This effect emerged across diverse representations of Animal that varied in valence (pets, farm animals, wild animals, and vermin; experiments 1-2). Non-White participants showed no such Human=Own Group bias (e.g., Black participants on a White-Black/Human-Animal IAT). However, when the test included two outgroups (e.g., Asian participants on a White-Black/Human-Animal IAT), non-White participants displayed Human=White associations. The overall effect was largely invariant across demographic variations in age, religion, and education but did vary by political ideology and gender, with self-identified conservatives and men displaying stronger Human=White associations (experiment 3). Using a variance decomposition method, experiment 4 showed that the Human=White effect cannot be attributed to valence alone; the semantic meaning of Human and Animal accounted for a unique proportion of variance. Similarly, the effect persisted even when Human was contrasted with positive attributes (e.g., God, Gods, and Dessert; experiment 5a). Experiments 5a-b clarified the primacy of Human=White rather than Animal=Black associations. Together, these experiments document a factually erroneous but robust Human=Own Group implicit stereotype among US White participants (and globally), with suggestive evidence of its presence in other socially dominant groups.

How do explicit and implicit evaluations shift? A preregistered meta-analysis of the effects of co-occurrence and relational information.

Based on 660 effect sizes obtained from 23,255 adult participants across 51 reports of experimental studies, this meta-analysis investigates whether and when explicit (self-reported) and implicit (indirectly revealed) evaluations reflect relational information (how stimuli are related to each other) over and above co-occurrence information (the fact that stimuli have been paired with each other). Using a mixed-effects metaregression, relational information was found to dominate over contradictory co-occurrence information in shifting both explicit (mean Hedges' g = 0.97, 95% CI [0.89, 1.05], 95% PI [0.24, 1.70]) and implicit evaluations (g = 0.27, 95% CI [0.19, 0.35], 95% PI [-0.46, 1.00]). However, considerable heterogeneity in relational effects on implicit evaluation made moderator analyses necessary. Implicit evaluations were particularly sensitive to relational information (a) in between-participant (rather than within-participant) designs; when (b) co-occurrence information was held constant (rather than manipulated); (c) targets were novel (rather than known); implicit evaluations were measured (d) first (rather than last) and (e) using an affect misattribution procedure (rather than an Implicit Association Test or evaluative priming task); and (f) relational and co-occurrence information were presented in temporal proximity (rather than far apart in time). Overall, the present findings suggest that both implicit and explicit evaluations emerge from a combination of co-occurrence information and relational information, with relational information usually playing the dominant role. Critically, variability in these effects highlights a need to refocus attention from existence proof demonstrations toward theoretical and empirical work on the determinants and boundary conditions of the influences of co-occurrence and relational information on explicit and implicit evaluations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).