In this part of the project, we explore the interaction with explanations of classifier decisions in the Interactive Machine Learning (IML) framework, which serves to improve ML models based on feedback gained from interaction with users. On the one hand, Explainable AI (XAI) is often considered a prerequisite for enabling meaningful interaction between user and machine, allowing the user to provide useful feedback based on which the model can be improved [
31,
94,
99]. On the other hand, IML might be a necessary component of optimal XAI systems, as users provided with model explanations desire to provide feedback in order to adjust the model [
86]. Hence, we hypothesize that investigating the application of IML approaches in an XAI context and vice versa can serve the goals of both paradigms. Building on related work exploring the explanation-feedback loop [
45,
89,
98], we will address the open questions of the best mechanism for integrating feedback into the model [
1], the type of feedback that is most helpful for model improvement, and how to best evaluate the framework, either in terms of model accuracy, or in terms of user-centric metrics. In [
33], we provide a survey on improving Natural Language Processing (NLP) models with different types of human explanations. We consider human explanations as a promising type of human feedback, as models can be trained more efficiently with human explanations compared to label-level feedback. The two most prominent types of human explanations used to improve NLP models are
highlight explanations, i.e. subsets of input elements that are deemed relevant for a prediction, and
free-text explanations [
103], i.e. natural language statements answering the question why an instance was assigned a specific label. We plan to focus our future efforts on learning from feedback in the form of natural language explanations, as users generally perceive natural language as preferred way of interacting with models, and natural language explanations are less constrained and can consequently have a higher information content than highlight explanations. In addition to enabling IML through XAI, we ask how IML methods can be used for best rendering domain narratives. Along with providing a means for general model improvement, the interaction between user and model can be exploited to adapt explanations, e.g. as personalized image descriptions that take into account the user’s active vocabulary [
15] or other features such as their preferred sentence length or level of detail. Our experiments in [
10] show promising initial results for caption personalization using interactive re-ranking of decoder output, which we plan to explore further in the future. In [
32], we outline an approach for using text- and image-based data augmentation to efficiently adapt image captioning models to new data based on user feedback. We plan to gain first insights on the effectiveness of these approaches based on simulated feedback, and to then consolidate findings in an interactive user study.