AI and the metaverse in the workplace: DEI opportunities and challenges
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
Purpose The metaverse, through artificial intelligence (AI) systems and capabilities, allows considerable data analysis in the workplace, largely exceeding traditional people analytics data collection. While concerns over surveillance and issues associated with privacy and discrimination have been raised, the metaverse has the potential to offer opportunities associated with fairer assessment of employee performance and enhancement of the employee experience, especially with respect to gender and race, inclusiveness and workplace equity. This paper aims at shedding light on the diversity, equity and inclusion (DEI) opportunities and challenges of implementing the metaverse in the workplace, and the role played by AI. Design/methodology/approach This paper draws on our past research on AI and the metaverse and provides insights addressed to human resources (HR) scholars and practitioners. Findings Our analysis of AI applications to the metaverse in the workplace sheds light on the ambivalent role of and potential trade-offs that may arise with this emerging technology. If used responsibly, the metaverse can enable positive changes concerning the future of work, which can promote DEI. Yet, the same technology can lead to negative DEI outcomes if implementations occur quickly, unsupervised and with a sole focus on efficiencies and productivity (i.e. collecting metrics, models etc.). Practical implications Managers and HR leaders should try to be first movers rather than followers when deciding if (or, better, when) to implement metaverse capabilities in their organizations. But how the metaverse is implemented will be strategic. This involves choices concerning the degree of invasive/pervasive monitoring (internal) as well as make or buy decisions concerning outsourcing AI capabilities. Originality/value Our paper is one among few (to date) that discusses AI capabilities in the metaverse at the intersection of the HR and information systems(IS) literature and that specifically tackles DEI issues. Also, we take a “balanced” approach when evaluating the metaverse from a DEI perspective. While most studies either demonize or celebrate these technologies from an ethical and DEI standpoint, we aim to highlight challenges and opportunities, with the goal to guide scholars and practitioners towards a responsible use of the metaverse in organizations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it