Safety Criteria and Safety Lifecycle for Artificial Neural Networks

There are many performance based techniques that aim to improve the safety of neural networks for safety critical applications. However, many of these techniques provide inadequate forms of safety arguments required for safety assurance. As a result, neural networks are typically restricted to advisory roles in safety-related applications. Neural networks are appealing to use given their ability to operate in unpredictable and changing environments. Therefore, it is desirable to certify them for highly-dependable roles in safety critical systems. This paper outlines the safety criteria which if enforced, would contribute to justifying the safety of neural networks. The criteria are a set of safety requirements for the behaviour of neural networks. A potential neural network model is also outlined and is based upon representing knowledge in symbolic form. The paper also presents a safety lifecycle for artificial neural networks. This lifecycle focuses on managing behaviour represented by neural networks and contributes to providing acceptable forms of safety assurance.