Virtual virtues: a governance framework of moral consequentialism and deontological ethics for immersive virtual reality platforms
Bibliographic record
Abstract
• Recent advancements in AI and VR have given rise to immersive platforms, where users can interact with human-like AI-enabled agents. • The intersection of physical and VR presents complexities in the ramifications of user interactions, necessitating new governance frameworks. • We present a governance framework integrating moral consequentialism and deontological ethics to assess and guide interactions in VR platforms. • We highlight the strategic implications of governance for technology to align with societal expectations while promoting inclusive VR platforms. • We extend governance discussions emphasizing the role of VR in reshaping immersive interactions, digital inclusion, and responsible AI adoption. Recent technological advancements have enabled immersive platforms to support interactions with human-like artificial agents, which raises novel ethical questions, as users may act under the premise of not facing tangible consequences. The intersection of physical and virtual environments, therefore, requires novel governance mechanisms that can address both the individual behaviours as well as the organizational implications of immersive platforms. In this conceptual paper, we incorporate a value-reflexive perspective to outline the foundations of moral judgments and bring together two schools of thought in ethical decision-making: consequentialism, which focuses on outcomes, and deontology, which emphasizes intentions. Building on these perspectives, we introduce a governance framework for immersive platforms that highlights how organizations can strategically align ethical principles with their broader objectives. Our proposed framework argues that the governance of immersive platforms is an ethical necessity as well as a strategic organizational capability. By considering employee interactions, user experiences, and stakeholder perceptions of fairness, the proposed governance of immersive platforms can inform diversity, equity, and inclusion initiatives and open new avenues for research at the intersection of digital ethics, governance, and strategy.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".