Toward Inclusive and Ethical Adoption of Autonomous Vehicles in Smart Urban Environments
Bibliographic record
Abstract
Autonomous Vehicles (AVs) are becoming central to the mobility infrastructure of smart cities, promising safer, cleaner, and more efficient transportation. Yet, their adoption also introduces significant ethical, social, and regulatory challenges that extend far beyond technical deployment. This paper presents a multi-dimensional framework grounded in Rawlsian justice theory and Value-Sensitive Design to evaluate and guide the ethical integration of AVs into urban ecosystems. Drawing on empirical evidence and simulation using the Cityscapes dataset, we identify key areas of concern including perception bias, spatial inequity in AV deployment, data privacy risks, and surveillance implications. Our findings reveal that AV systems, if unregulated, may exacerbate digital redlining and reinforce socio-economic disparities. We propose ethical risk modeling and decentralized governance mechanisms to mitigate these risks and promote transparency, inclusivity, and sustainability. By combining theoretical ethics with technical simulation and policy analysis, this study offers a comprehensive roadmap for cities aiming to align AV innovation with principles of justice, trust, and resilience.
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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 | 0.036 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".