Perspective matters: a systematic review of immersive virtual reality to reduce racial prejudice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In the wake of the COVID-19 pandemic and the rise of social justice movements, increased attention has been directed to levels of intergroup tension worldwide. Racial prejudice is one such tension that permeates societies and creates distinct inequalities at all levels of our social ecosystem. Whether these prejudices are present explicitly (directly or consciously) or implicitly (unconsciously or automatically), manipulating body ownership by embodying an avatar of another race using immersive virtual reality (IVR) presents a promising approach to reducing racial bias. Nevertheless, research findings are contradictory, which is possibly attributed to variances in methodological factors across studies. This systematic review, therefore, aimed to identify variables and methodological variations that may underlie the observed discrepancies in study outcomes. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review encompassed 12 studies that employed IVR and embodiment techniques to investigate racial attitudes. Subsequently, two mini meta-analyses were performed on four and five of these studies, respectively — both of which utilised the Implicit Association Test (IAT) as a metric to gauge these biases. This review demonstrated that IVR allows not only the manipulation of a sense of body ownership but also the investigation of wider social identities. Despite the novelty of IVR as a tool to help understand and possibly reduce racial bias, our review has identified key limitations in the existing literature. Specifically, we found inconsistencies in the measures and IVR equipment and software employed, as well as diversity limitations in demographic characteristics within both the sampled population and the embodiment of avatars. Future studies are needed to address these critical shortcomings. Specific recommendations are suggested, these include: (1) enhancing participant diversity in terms of the sample representation and by integrating ethnically diverse avatars; (2) employing multi-modal methods in assessing embodiment; (3) increasing consistency in the use and administration of implicit and explicit measures of racial prejudice; and (4) implementing consistent approaches in using IVR hardware and software to enhance the realism of the IVR experience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it