Unlike in high-stakes decision-making contexts like medicine or law enforcement, where tabular data is prevalent and commonly available, stock market analysis relies on transparent access to the associated longitudinal data. Similar to the development in different domains, researchers are also attempting to increase predictive performance through the use of artificial intelligence (AI) in the detection of anomalies in time series, thereby reducing the risk of erroneous decisions by human end users. However, the low interpretability of the underlying AI models, if not properly addressed, can also lead to problematic outcomes. If end users cannot detect erroneous reasoning within an AI model’s anomaly detection process, they either tend not to use the system due to their low confidence, or they tend to put too much trust into the system due to not being able to question its outputs. To mitigate both of these problems, researchers have developed Explainable AI (XAI) methods that aim to make AI models scrutable and understandable to human end users. A majority of these methods, though, are intended for use on tabular data.

This thesis project reviews existing XAI methods for time series data in a systematic literature review (SLR). Based on these insights and interviews to elicit requirements for the project, the student develops an AI-based anomaly detection system for stock market data that utilizes a selection of XAI methods found in the SLR. The student evaluates their developed system by conducting a series of expert interviews. These will be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).

In order to properly utilize performance improvements through the adoption of artificial intelligence (AI) models, a number of conditions must be met. Since modern deep learning systems are opaque and inscrutable to human users, problems of mistrust and corresponding non-use can arise. But even if the adoption of AI technology into clinical practice is not hindered by such barriers, problems may arise due to an attitude of overconfidence and overreliance on AI results. The explainable AI (XAI) community strives to develop methods that help to create an appropriate level of trust in AI systems. Such methods are particularly important in the medical application context, as incorrect diagnostic and prognostic decisions can have significant negative consequences for the patients concerned. We intend to research XAI in the context of medical decision support systems. This includes developing an understanding of the application of XAI to different data types and diseases, and whether there has been experimental evaluation of the impact of XAI in AI-based decision support.

To develop a better understanding of XAI in the context of medical decision support systems, a systematic literature review (SLR) is carried out in this Bachelor’s thesis. To collect additional data and enhance the knowledge about XAI use cases in medical practice, the student conducts a series of expert interviews for requirements elicitation. 

Notifications from instant messaging applications can interrupt employees' productive time. While there are different ways to influence the notification behavior of instant messengers, such as turning off the application or muting notifications for certain periods of time, these measures require self-discipline and/or often result in missing notifications when not in flow. We have developed an adaptive instant messaging blocker that aims to solve this problem by recognizing the user's flow state at predefined intervals, based on their physiological data and using machine learning methods. As soon as a flow state is recognized, the “do not disturb” status is automatically activated for the duration of the flow state.

We conducted interviews with knowledge workers to evaluate the developed system. Therefore, a qualitative analysis (with MAXQDA) is to be carried out in this Master's thesis in order to evaluate the system on the basis of the interviews conducted.