Towards Ethical Risk Assessment of Symbiotic AI Systems with Fuzzy Rules

Artificial Intelligence (AI) based systems are expanding rapidly in all domains of life. They are entering our everyday life and performing tasks on our behalf. AI-based systems such as personal healthcare assistants are increasingly engaging in close symbiotic relationships with humans. Symbiotic AI (SAI) promises improved outcomes in various domains such as healthcare, education, and business. However, as the degree of symbiosis increases, so does the ethical risk. To ensure that these systems behave ethically and do not cause harm of any kind (physical, mental, violation of privacy, etc.), we need to find ways to assess the ethical risk (risk of causing harm), then choose the right action to mitigate that risk. In this work, we propose an approach based on fuzzy logic for ethical risk assessment (ERA) of SAI systems. The approach is illustrated by means of a case study taken from the healthcare domain.