Funding Source: SNF

Wearable devices, combined with Artificial Intelligence (AI) methods, can bring significant and sustainable improvements to our lives. Affective computing utilizes the combination of wearables and AI to provide methods for tracking factors related to affective states (e.g., emotions and stress). Such methods serve as the first step toward remote mental-health management tools, urgently needed in Switzerland and worldwide. Unfortunately, today’s groundbreaking AI methods are black-boxed (i.e., the decision model and the process are not understandable) and require vast amounts of data – data that are particularly sensitive in the case of affective computing. Hence, it is evident that explainable and privacy-aware affective computing is vital – what can be more private than our emotions, and how can we be sure that the AI “got it right”?  This project has three main objectives to pave the way toward eXplAInable and Private Affective Computing (XAI-PAC): 

1.  To develop novel eXplainable AI (XAI) tools for affective-computing methods based on wearable sensor data. The novel XAI tools will be multimodal and interactive, enabling the explanation of ML models.
2.  To develop privacy-aware ML methods for wearable sensor data, including privacy-aware personalization and domain-adaptation.
3. To fuse privacy awareness and explainability into a single approach. This third objective leaps beyond the independent realization of XAI and privacy-aware ML.