Perceived Trustworthiness of Natural Language Generators
Beatriz Cabrero-Daniel Andrea Sanagustín Cabrero
Natural Language Generation tools, such as chatbots that can generate
human-like conversational text, are becoming more common both for personal and
professional use. However, there are concerns about their trustworthiness and
ethical implications. The paper addresses the problem of understanding how
different users (e.g., linguists, engineers) perceive and adopt these tools and
their perception of machine-generated text quality. It also discusses the
perceived advantages and limitations of Natural Language Generation tools, as
well as users' beliefs on governance strategies. The main findings of this
study include the impact of users' field and level of expertise on the
perceived trust and adoption of Natural Language Generation tools, the users'
assessment of the accuracy, fluency, and potential biases of machine-generated
text in comparison to human-written text, and an analysis of the advantages and
ethical risks associated with these tools as identified by the participants.
Moreover, this paper discusses the potential implications of these findings for
enhancing the AI development process. The paper sheds light on how different
user characteristics shape their beliefs on the quality and overall
trustworthiness of machine-generated text. Furthermore, it examines the
benefits and risks of these tools from the perspectives of different users.