Deconstructing demographic bias in speech-based machine learning models for digital health.

ABSTRACT:

Introduction

Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes.

Methods

This paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via usi

SUBMITTER: Yang M 

PROVIDER: S-EPMC11306200 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature