Software Transparency as a Key Requirement for Self-Driving Cars
Bibliographic record
Abstract
Self-Driving cars is a fast-growing area of study both in academia and industry. It is part of a broader domain which involves the development of software for Highly Automated Vehicles (HAV) and notions extracted from Artificial Intelligence/Autonomous Systems (AI/AS). There are many challenges that must be overcome to deliver self-driving cars in a manner that is readily accepted by consumers and society. Studies have shown that although many people are comfortable with the idea of AI helping them to operate their houses or schedule appointments, not many people are comfortable with the idea of cars being driven by AI algorithms. At the same time, insurance companies are concerned about vehicle liability issues and how to demonstrate who/what caused an accident. We believe that self-driving cars that demonstrate transparency in their operations will increase consumer trust which is pivotal to its acceptance and will pave the way for its commercialization and daily use. In this work, we investigate how to pursue the elicitation and modeling of transparency as a Non-Functional Requirement (NFR) to produce self-driving cars that are more robust.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".