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Key Takeaways

Explainability alone cannot guarantee safety: While XAI methods help build trust and confidence in ML systems, the paper concludes that explainability is "not sufficient in themselves to assure safety" in healthcare applications, meaning additional safety measures beyond interpretability are necessary.
Traditional safety approaches don't work for ML systems: Established methods for assuring safety in critical systems fail when applied to machine learning because ML models lack "clear, pre-defined specifications" against which to measure validity, creating a fundamental challenge for healthcare deployment.
XAI methods can produce safety evidence: The paper demonstrates through a clinical case study on mechanical ventilation weaning that explainability techniques can generate concrete evidence supporting safety arguments, showing XAI's practical value in documenting why an ML system's decisions are trustworthy.
The "black box" problem has real consequences in healthcare: The opacity of ML models makes them resistant to human scrutiny, which is particularly problematic in safety-critical medical contexts where clinicians need to understand and verify system recommendations before patient care decisions.
XAI should be integrated into formal safety cases: Rather than treating explainability as optional, the paper shows that XAI methods should be systematically incorporated into safety arguments and documentation to demonstrate where and how ML systems meet safety requirements in healthcare settings.

Abstract

Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing ML where there is no clear, pre-defined specification against which to assess validity. This problem is exacerbated by the "opaque" nature of ML where the learnt model is not amenable to human scrutiny. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human-interpretable representations of ML models which can help users to gain confidence and build trust in the ML system. However, little work explicitly investigates the role of explainability for safety assurance in the context of ML development. This paper identifies ways in which XAI methods can contribute to safety assurance of ML-based systems. It then uses a concrete ML-based clinical decision support system, concerning weaning of patients from mechanical ventilation, to demonstrate how XAI methods can be employed to produce evidence to support safety assurance. The results are also represented in a safety argument to show where, and in what way, XAI methods can contribute to a safety case. Overall, we conclude that XAI methods have a valuable role in safety assurance of ML-based systems in healthcare but that they are not sufficient in themselves to assure safety.

Key Takeaways

Explainability alone cannot guarantee safety: While XAI methods help build trust and confidence in ML systems, the paper concludes that explainability is "not sufficient in themselves to assure safety" in healthcare applications, meaning additional safety measures beyond interpretability are necessary.
Traditional safety approaches don't work for ML systems: Established methods for assuring safety in critical systems fail when applied to machine learning because ML models lack "clear, pre-defined specifications" against which to measure validity, creating a fundamental challenge for healthcare deployment.
XAI methods can produce safety evidence: The paper demonstrates through a clinical case study on mechanical ventilation weaning that explainability techniques can generate concrete evidence supporting safety arguments, showing XAI's practical value in documenting why an ML system's decisions are trustworthy.
The "black box" problem has real consequences in healthcare: The opacity of ML models makes them resistant to human scrutiny, which is particularly problematic in safety-critical medical contexts where clinicians need to understand and verify system recommendations before patient care decisions.
XAI should be integrated into formal safety cases: Rather than treating explainability as optional, the paper shows that XAI methods should be systematically incorporated into safety arguments and documentation to demonstrate where and how ML systems meet safety requirements in healthcare settings.

Abstract

The Role of Explainability in Assuring Safety of Machine Learning in Healthcare

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

The Role of Explainability in Assuring Safety of Machine Learning in Healthcare

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links