Using Nominal Group Technique to Identify Key Ethical Concerns Regarding Hearing Aids With Machine Learning

Abstract

Purpose: Machine learning (ML) in new-generation hearing aid technology presents a beneficial opportunity for development in audiology. It is, however, important to balance new applications against the audiologist's professional ethics to protect the client from any harm. This study aimed to identify the key ethical concerns related to the latest digital hearing aid technology that incorporates ML that could potentially impact the client and/or the audiologist. Method: A nominal group technique was conducted with the audiologist to generate and prioritize a list of key ethical concerns related to human data interaction, with specific focus on hearing aids. Results: Five categories were identified in relation to the potential impact on the client: (a) privacy and confidentiality, (b) relationship and trust, (c) nonmaleficence, (d) informed decision making, and (e) financial gain. An additional five categories were identified in relation to the potential impact on the audiologist: (a) privacy and confidentiality, (b) professional responsibility, (c) relationship and trust, (d) financial gain, and (e) trust technology. Privacy and confidentiality were ranked as the highest priority that should be considered when supplying clients with the latest hearing aid technology. Conclusions: Hearing aid technology has evolved considerably, and audiologists need to keep abreast of and master the general technological developments and the associated ethical challenges that may arise. Discussions on the ethics related to ML and hearing aid fittings will help identify the key ethical concerns involved and, thereby, enhance the ethical sensitivity of the profession.