How helping students design ethical metaverse platforms can lead to safety and well being for all
Bibliographic record
Abstract
A lack of proximity and enhanced anonymity in virtual worlds seems to provide the license to Artificial Intelligence (AI) based metaverse users to misbehave. Bullying, abuse, spread of hatred and divisiveness and manipulations of minds in Metaverses are growing exponentially due to the speed and magnitude with which AI enabled bots in Metaverses can multiply and reproduce content. Online violence has begun spilling into the real world which is negatively impacting the psyche and wellbeing of children and young adults in society. Ethicists or well-meaning employees have spoken out against these violations in Metaverses. But we find many such ethics groups have been dissolved or silenced while employees who are whistleblowers are often fired, discredited, or dismissed. Business Ethics, Marketing, Management and Sustainability students are often asked to simply carry out an ethical analysis of cases and provide recommendations. While such processes have helped explore various ethical schools of thought, the application of these concepts to AI based metaverses seems less about what framework to apply and more about how to design a fail-proof system to protect the safety and wellbeing of all. Such an approach will make students more aware of the consequences of their choices and develop a sense of responsibility towards the wellbeing of all. The paper proposes the use of a case study of a hypothetical company that has a metaverse platform and the challenges it faces in addressing abuse and scandals on its platform. The paper also offers a detailed review along with the pros and cons of several ethical frameworks and puts forth two key questions to students asking them to design a metaverse platform that can, a) ensure the wellbeing, security, and safety of the users who are not even aware that their minds may be swayed and manipulated; and b) find a way to convince companies that create AI-based metaverses to adopt ethical frameworks. Sample answers are provided to help faculty work with students understand the importance of always designing products and services with personal and others’ well-being in mind rather than only making profits at the cost of people’s safety and security.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".