Ethical Considerations in the Use of Disfluencies in AI-Generated Speech

Abstract

Disfluency occurs regularly in natural, everyday speech. Such phenomena as silent pauses, filled pauses (uh, um in English) and repairs occur with regular frequency and training data for natural language speech generation systems may be replete with these. This raises the question whether these items can be used productively in such systems in educational contexts and whether, as non-authentic phenomena, they can be used ethically. This position paper takes the five principles of AI ethics—beneficence, non-maleficence, autonomy, justice, and explicability (Floridi and Cowls, 2019)—as a starting point and proposes the Disfluency Instrumentality Audit as a tool to evaluate the ethical considerations of disfluency in AI-generated speech. The facilitative nature of disfluencies is explained in detail in order to argue for the potential beneficent nature of these phenomena in educational contexts. Sample scenarios are presented and discussed in order to illustrate how the audit might be used to evaluate the ethical considerations. Although presented here as applying to educational contexts, the audit would be applicable to wider contexts involving AI-speech generation for interactive agents.