DO-178 Compliance Considerations for Artificial Intelligent Software
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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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 | 0.058 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".