Two-step counterfactual generation for OOD examples

However, they still make erroneous predictions when exposed to inputs from an unfamiliar distribution. This poses a significant obstacle to the deployment of ML models in safety-critical applications such as healthcare and autonomous vehicles. Consequently, for applications in these domains, two fundamental requirements for the deployment of ML models are; 1) being able to identify data that is from a different distribution from the data on which the model was trained, which is referred to as out-of-distribution (OOD) detection, outlier detection, or anomaly detection [30]; 2) being able to explain the prediction of the model [24]. There has been significant work on improving the accuracy of OOD detectors although, there has not been much work on explaining why a data point is OOD [20]. As OOD detection algorithms are increasingly used in safety-critical domains, providing explanations for high-stakes decisions has become an ethical and regulatory requirement [26]. Therefore, it is important to develop methods that provide both accurate OOD scores and also provide an explanation of why specific data points are detected as OOD. OOD detection can be considered a binary classification problem, where a data point can belong either to the in-distribution (ID) class or to the OOD class [4]. Additionally, there are different versions of the OOD detection problem, which are referred to as near-OOD and far-OOD detection [23, 29]. OOD data points that have neither non-discriminative (class-irrelevant) nor discriminative (class-relevant) features are referred to as far-OOD data and are therefore very dissimilar to the ID data.