In the following article, we introduce a novel workflow, which we subsume under the term “explainable cooperative machine learning” and show its practical application in a data annotation and model training tool called NOVA. The main idea of our approach is to interactively incorporate the ‘human in the loop’ when training classification models from annotated data. In particular, NOVA offers a collaborative annotation backend where multiple annotators join their workforce. A main aspect is the possibility of applying semi-supervised active learning techniques already during the annotation process by giving the possibility to pre-label data automatically, resulting in a drastic acceleration of the annotation process. Furthermore, the user-interface implements recent eXplainable AI techniques to provide users with both, a confidence value of the automatically predicted annotations, as well as visual explanation. We show in an use-case evaluation that our workflow is able to speed up the annotation process, and further argue that by providing additional visual explanations annotators get to understand the decision making process as well as the trustworthiness of their trained machine learning models.

With the ongoing rise of machine learning, the need for methods for explaining decisions made by artificial intelligence systems is becoming a more and more important topic. Especially for image classification tasks, many state-of-the-art tools to explain such classifiers rely on visual highlighting of important areas of the input data. Contrary, counterfactual explanation systems try to enable a counterfactual reasoning by modifying the input image in a way such that the classifier would have made a different prediction. By doing so, the users of counterfactual explanation systems are equipped with a completely different kind of explanatory information. However, methods for generating realistic counterfactual explanations for image classifiers are still rare. Especially in medical contexts, where relevant information often consists of textural and structural information, high-quality counterfactual images have the potential to give meaningful insights into decision processes. In this work, we present GANterfactual, an approach to generate such counterfactual image explanations based on adversarial image-to-image translation techniques. Additionally, we conduct a user study to evaluate our approach in an exemplary medical use case. Our results show that, in the chosen medical use-case, counterfactual explanations lead to significantly better results regarding mental models, explanation satisfaction, trust, emotions, and self-efficacy than two state-of-the art systems that work with saliency maps, namely LIME and LRP.

In this paper, we introduce a next-generation annotation tool called NOVA, which implements a workflow that interactively incorporates the 'human in the loop'. In particular, NOVA offers a collaborative annotation backend where multiple annotators join their workforce. A main aspect of NOVA is the possibility of applying semi-supervised active learning where Machine Learning techniques are used already during the annotation process by giving the possibility to pre-label data automatically. Furthermore, NOVA implements recent eXplainable AI (XAI) techniques to provide users with both, a confidence value of the automatically predicted annotations, as well as visual explanation. This way, annotators get to understand whether they can trust their ML models, or more annotated data is necessary.

In this article, we introduce a next-generation annotation tool called NOVA for emotional behaviour analysis, which implements a workflow that interactively incorporates the 'human in the loop'. A main aspect of NOVA is the possibility of applying semi-supervised active learning where Machine Learning techniques are used already during the annotation process by giving the possibility to pre-label data automatically. Furthermore, NOVA implements recent eXplainable AI (XAI) techniques to provide users with both, a confidence value of the automatically predicted annotations, as well as visual explanations. We investigate how such techniques can assist non-experts in terms of trust, perceived self-efficacy, cognitive workload as well as creating correct mental models about the system by conducting a user study with 53 participants. The results show that NOVA can easily be used by non-experts and lead to a high computer self-efficacy. Furthermore, the results indicate that XAI visualisations help users to create more correct mental models about the machine learning system compared to the baseline condition. Nevertheless, we suggest that explanations in the field of AI have to be more focused on user-needs as well as on the classification task and the model they want to explain.

In this paper, we explore the benefits of our next-generation annotation and analysis tool NOVA in the domain of psychotherapy. The NOVA tool has been developed, tested and applied in behaviour studies for several years and psychotherapy sessions offer a great way to expand areas of application into a challenging yet promising field. In such scenarios, interactions with patients are often rated by questionnaires and the therapist's subjective rating, yet a qualitative analysis of the patient's non-verbal behaviours can only be performed in a limited way as this is very expensive and time-consuming. A main aspect of NOVA is the possibility of applying semi-supervised active learning where Machine Learning techniques are already used during the annotation process by giving the possibility to pre-label data automatically. Furthermore, NOVA provides therapists with a confidence value of the automatically predicted annotations. This way, also non-ML experts get to understand whether they can trust their ML models for the problem at hand.