Ethical trade-offs in AI for mental health

TL;DR

This paper discusses how AI in mental health can never be completely objective and highlights the need for transparency about ethical choices in its development.

Contribution

The paper introduces the idea that algorithmic objectivity in mental health is unattainable due to human values and biases.

Findings

Algorithms in psychiatry are influenced by human values and biases.

Transparency about ethical trade-offs is essential in algorithm development for mental health.

Abstract

It is expected that machine learning algorithms will enable better diagnosis, prognosis, and treatment in psychiatry. A central argument for deploying algorithmic methods in clinical decision-making in psychiatry is that they may enable not only faster and more accurate clinical judgments but also that they may provide a more objective foundation for clinical decisions. This article argues that the outputs of algorithms are never objective in the sense of being unaffected by human values and possibly biased choices. And it suggests that the best way to approach this is to ensure awareness of and transparency about the ethical trade-offs that must be made when developing an algorithm for mental health.