DO-178 Compliance Considerations for Artificial Intelligent Software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) in safety-critical aerospace systems has introduced new challenges in ensuring compliance with regulatory standards. DO-178C, the de facto standard for software development in the aerospace industry, provides guidelines for the development of safety-critical software. However, its applicability to AI software is not straightforward. This paper discusses the DO-178C compliance considerations for AI software, highlighting the unique challenges and opportunities presented by AI. We examine the following key aspects: 1) Software Requirements, the need for clear, concise, and unambiguous requirements for AI software, and how to ensure that these requirements are properly validated and verified; 2) Software Design, the implications of AI software design on DO-178C compliance, including the use of machine learning algorithms, neural networks, and training data; 3) Software Verification, the use of testing, validation, and formal methods to ensure that the software meets its requirements; and 4) Software Configuration Management, the importance of software configuration management in ensuring the integrity and traceability of AI software, and how to implement these practices in a DO-178C compliant manner. DO-178C compliance is essential for ensuring the safety and reliability of AI software in safety-critical aerospace systems. By understanding the unique challenges and opportunities presented by AI, developers can ensure that their software meets the requirements of DO-178C, and that it is safe, reliable, and effective.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it