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Explainable AI for Health Sensing


Description The success of machine learning research has lead to an increase in potential applications, especially in the health domain. However, many contemporary systems are essentially black boxes; the internal operations determining their outputs are not transparent. Especially in the health domain, those developing machine-learning systems should be able to explain their rationale and characterise their strengths and weaknesses.
Task Explore the efficacy of different explainable AI techniques with a focus on health
Utilises Python, potentially deep learning toolkits
Requirements Machine learning knowledge a plus
Languages English
Supervisor Dr. Nicholas Cummins (nicholas.cummins@informatik.uni-augsburg.de)