Interviewing is one of the most predominant methods used in social science research and has the potential to produce valuable insight into questions that are at the heart of Human-Machine Communication (HMC) research: How do people conceptualize artificial intelligence (AI), robots, and related technologies as communicators? How do people understand and view the integration of these applications across spaces of everyday life? What are people most concerned and hopeful about regarding communicative AI? At the same time, scholars engaging in talk about “talking with machines” are likely to experience certain methodological challenges, some of which are rooted in the dynamics of conversation about emerging technology more generally while others are tied more specifically to the nature and function of AI and the linguistic features of the language being used for the research. This presentation outlines some of the most predominant challenges experienced by scholars, examining the reasons why they are present in HMC research and how they can impact how research is conducted and its findings. The presentation then puts forward strategies for thinking through how to best navigate the complexity of talking with people about AI. Because some of the challenges to interviews about AI are rooted in dynamics of communication and language, the methodological hurdles and solutions presented are also of relevance to research methods beyond interviewing and qualitative approaches.
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