ForDigitStress: presentation and evaluation of a new laboratory stressor using a digital job interview-scenario

Introduction

Since the COVID-19 pandemic, working environments and private lives have changed dramatically. Digital technologies and media have become more and more important and have found their way into nearly all private and work environments. Communication situations have been largely relocated to virtual spaces. One of these scenarios is digital job interviews. Job interviews are usually-also in the non-digital world-perceived as stressful and associated with biological stress responses. We here present and evaluate a newly developed laboratory stressor that is based on a digital job interview-scenario.

Methods

N = 45 healthy people participated in the study (64.4% female; mean age: 23.2 ± 3.6 years; mean body mass index = 22.8 ± 4.0 kg/m²). Salivary alpha-amylase (sAA) and cortisol were assessed as measures for biological stress responses. Furthermore, perceived stress was rated at the time points of the saliva samplings. The job interviews lasted between 20 and 25 min. All materials, including instructions for the experimenter (i.e., the job interviewer) and the data set used for statistical analysis, as well as a multimodal data set, which includes further measures, are publicly available.

Results

Typical subjective and biological stress-response patterns were found, with peak sAA and perceived stress levels observed immediately after the job interviews and peak cortisol concentrations 5 min afterwards. Female participants experienced the scenario as more stressful than male participants. Cortisol peaks were higher for participants who experienced the situation as a threat in comparison to participants who experienced it as a challenge. Associations between the strength of the stress response with further person characteristics and psychological variables such as BMI, age, coping styles, and personality were not found.

Discussion

Overall, our method is well-suited to induce biological and perceived stress, mostly independent of person characteristics and psychological variables. The setting is naturalistic and easily implementable in standardized laboratory settings.

GANterfactual—Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning

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.

eXplainable Cooperative Machine Learning with NOVA

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.