"Sorry I Didn't Hear You." The Ethics of Voice Computing and AI in High Risk Mental Health Populations

This article examines the ethical and policy implications of using voice computing and artificial intelligence to screen for mental health conditions in low income and minority populations. Mental health is unequally distributed among these groups, which is further exacerbated by increased barriers to psychiatric care. Advancements in voice computing and artificial intelligence promise increased screening and more sensitive diagnostic assessments. Machine learning algorithms have the capacity to identify vocal features that can screen those with depression. However, in order to screen for mental health pathology, computer algorithms must first be able to account for the fundamental differences in vocal characteristics between low income minorities and those who are not. While researchers have envisioned this technology as a beneficent tool, this technology could be repurposed to scale up discrimination or exploitation. Studies on the use of big data and predictive analytics demonstrate that low income minority populations already face significant discrimination. This article urges researchers developing AI tools for vulnerable populations to consider the full ethical, legal, and social impact of their work. Without a national, coherent framework of legal regulations and ethical guidelines to protect vulnerable populations, it will be difficult to limit AI applications to solely beneficial uses. Without such protections, vulnerable populations will rightfully be wary of participating in such studies which also will negatively impact the robustness of such tools. Thus, for research involving AI tools like voice computing, it is in the research community's interest to demand more guidance and regulatory oversight from the federal government.