Care work in long-term care (LTC) is considered as a genuine human-centred activity, requiring empathy, emotional investment, physical encounters and intimate, trust-based relations between various care-givers and care-recipients. AI technologies are introduced in this professional field to assist care workers in their daily activities and provide an additional measure of care for clients. This has changed the provision of care, affecting care givers and recipients alike.
So far, little research has been done on the biases that emerge from AI in this field and the risks that algorithmic governance of care offers in the profession. Based on data generated by AI technologies, unfair decisions can remain unnoticed in the process of linking different big data sets, leading to ethical and social issues in LTC.
ALGOCARE’s goal is to understand the functionality of algorithmic governing systems of care and their effects on care givers and recipients. Insight from ethnographic research in LTC will provide an
understanding of the impact and needs of care in relation to AI systems. The use-value of eXplainable AI (XAI) methods (trustworthiness, privacy awareness, explainable procedures) and different levels of transparency that either the model itself provides or methods that provide them after development are explored. Based on this insight, metrics are developed to evaluate the eXplainability of machine learning models for care.