A Justice-First Approach to Ambient Intelligence in Healthcare
Jonathan Herington, Mildred K. Cho
Abstract
Ambient intelligence systems (AIS) are increasingly deployed to provide persistent, artificially intelligent, monitoring and documentation of healthcare. AIS pose many ethical issues, including risks to the privacy of third parties, pernicious biases in predictive analytics, and intractable conflicts between the interests of patients, family members and care providers. In this paper we argue that these risks cannot be effectively navigated by applying a traditional bioethical framework. The traditional bioethical framework focuses heavily on protecting the autonomy and interests of a patient within the context of a single decision. An AIS, on the other hand, occupies a physical space and thus implicates multiple stakeholders, with interests that may conflict, in a setting where individually opting out of the interaction may be impractical or infeasible. Hence, we argue that, like many questions arising in the context of public health ethics, they should be dealt with through a "justice-first" approach to ethical theorizing.