Responsible AI Framework for Autonomous Vehicles: Addressing Bias and Fairness Risks
Bibliographic record
Abstract
Autonomous Vehicles (AVs) hold immense potential to revolutionize transportation, yet their deployment raises significant concerns regarding safety, security, and ethical considerations. Furthermore, the increased use of automation, edge computing, and Artificial Intelligence (AI), fueled by generative AI technology, has increasingly elevated the risks of AI. Most of the existing research only covers ethical components and lacks the breadth of Responsible AI (RAI), while some have presented components such as justice and solidarity but do not provide an approach or mechanism to implement those in an AI-based system. This paper addresses these gaps by proposing a comprehensive RAI framework for AVs with four key contributions. First, it provides an in-depth analysis of AI risks in AVs, encompassing safety, security, ethical, and legal domains, offering a structured classification to guide developers and regulators. Second, it introduces a holistic RAI framework that spans the AI lifecycle, identifying and mitigating risks at each AV system development and deployment stage. Third, the paper focuses on bias and fairness within AV systems, outlining precise techniques for bias detection and mitigation across data collection, algorithm design, and real-time decision-making. Lastly, the research presents bias removal simulations using publicly available AV datasets, evaluating mitigation strategies such as synthetic data generation and algorithmic fairness analysis. The proposed framework and simulations provide a practical foundation for integrating RAI principles into AV development, ensuring safer, fairer, and more accountable AI-driven mobility solutions.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".