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Abstract Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI’s negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions (“smart replies”), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly. 

As AI-mediated communication (AI-MC) becomes more prevalent in everyday interactions, it becomes increasingly important to develop a rigorous understanding of its effects on interpersonal relationships and on society at large. Controlled experimental studies offer a key means of developing such an understanding, but various complexities make it difficult for experimental AI-MC research to simultaneously achieve the criteria of experimental realism, experimental control, and scalability. After outlining these methodological challenges, this paper offers the concept of methodological middle spaces as a means to address these challenges. This concept suggests that the key to simultaneously achieving all three of these criteria is to abandon the perfect attainment of any single criterion. This concept's utility is demonstrated via its use to guide the design of a platform for conducting text-based AI-MC experiments. Through a series of three example studies, the paper illustrates how the concept of methodological middle spaces can inform the design of specific experimental methods. Doing so enabled these studies to examine research questions that would have been either difficult or impossible to investigate using existing approaches. The paper concludes by describing how future research could similarly apply the concept of methodological middle spaces to expand methodological possibilities for AI-MC research in ways that enable contributions not currently possible. 

AI-mediated communication (AI-MC) represents a new paradigm where communication is augmented or generated by an intelligent system. As AI-MC becomes more prevalent, it is important to understand the effects that it has on human interactions and interpersonal relationships. Previous work tells us that in human interactions with intelligent systems, misattribution is common and trust is developed and handled differently than in interactions between humans. This study uses a 2 (successful vs. unsuccessful conversation) x 2 (standard vs. AI-mediated messaging app) between subjects design to explore whether AI mediation has any effects on attribution and trust. We show that the presence of AI-generated smart replies serves to increase perceived trust between human communicators and that, when things go awry, the AI seems to be perceived as a coercive agent, allowing it to function like a moral crumple zone and lessen the responsibility assigned to the other human communicator. These findings suggest that smart replies could be used to improve relationships and perceptions of conversational outcomes between interlocutors. Our findings also add to existing literature regarding perceived agency in smart agents by illustrating that in this type of AI-MC, the AI is considered to have agency only when communication goes awry.