LAUSR

The prospect of speech analysis by means of technologies based on natural language processing (NLP) lies in the anticipated ability of algorithms to hear what humans cannot. The premise is that even experienced psychiatrists dedicating their full attention to the patient cannot be expected to pick up on all the granular signals that might be present in the patient’s speech or to utilize the complex relationships between those signals. Because of the limitations inherent in human data processing capacities, potentially useful information in patient speech might just be “noise” to the psychiatrist. As such, it might not be perceived as carrying meaningful information that can be used in a clinical assessment of the patient. NLP-based models can be implemented into clinical decision support systems (NLP-CDS) and give psychiatrists “hearing aid,” thus improving assessments through automated analysis of acoustic as well as semantic features of the patient’s speech. However, NLP-based speech analysis in psychiatry invokes some of the most salient legal and ethical challenges that are known from the more general discourse around artificial intelligence (AI). Automated speech recognition systems have been suggested to perform disparately across ethnic groups,1 and machine learning (ML) algorithms are likely to reflect historical biases when they are applied to natural language.2,3 Moreover, currently available methods for interpretation of complex ML/NLP systems appear to be inadequate as a means of detecting potentially discriminatory behavior.4