Perspective Matters: A Systematic Review of Immersive Virtual Reality to Reduce Racial Prejudice
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 present explicitly (directly or consciously) or implicitly (unconsciously or automatically), research suggests that manipulating body ownership by embodying an avatar of another race using immersive virtual reality (IVR) can reduce racial bias. 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. However, 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 employed, as well as demographic characteristics within both the sampled population and the embodiment of avatars. Future studies are needed to address these critical shortcomings by appropriately utilising implicit and explicit measures of racial prejudice, ensuring diverse sample representation, and considering a broader spectrum of embodied social groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".