Jaime Banks

Jaime Banks

In discussions of human relations with conversational agents (CAs; e.g., voice assistants, AI companions, some social robots), they are increasingly referred to as parasocial. This is a misapplication of the term, heuristically taken up to mean "unreal." In this provocation, I briefly account for the theoretical trajectory of parasociality and detail why it is inaccurate to apply the notion to human interactions with CAs. In short, "parasocial" refers to a human-character relations that are one-sided, non-dialectical, character-governed, imagined, vicarious, predictable, and low-effort; the term has been co-opted to instead refer to relations that are seen as unreal or invalid. The scientific problematics of this misapplication are nontrivial. They lead to oversimplification of complex phenomena, misspecified variables and misdiagnosed effects, and devaluation of human experiences. Those challenges, in turn, have downstream effects on norms and practice. It is scientifically, practically, and ethically imperative to recognize the sociality of human-agent relations.

Jaime Banks

The mainstreaming of companionable machines--customizable artificial agents designed to participate in ongoing, idiosyncratic, socioemotional relationships--is met with relative theoretical and empirical disarray, according to recent systematic reviews. In particular, the conceptualization and measurement of machine companionship (MC) is inconsistent or sometimes altogether missing. This study starts to bridge that gap by developing and initially validating a novel measurement to capture MC experiences--the unfolding, autotelic, positively experienced, coordinated connection between human and machine--with AI companions (AICs). After systematic generation and expert review of an item pool (including items pertaining to dyadism, coordination, autotelicity, temporality, and positive valence), N = 467 people interacting with AICs responded to the item pool and to construct validation measures. Through exploratory factor analysis, two factors were induced: Eudaimonic Exchange and Connective Coordination. Construct validation analyses (confirmed in a second sample; N = 249) indicate the factors function largely as expected. Post-hoc analyses of deviations suggest two different templates for MC with AICs: One socioinstrumental and one autotelic.

Jaime Banks, Nicholas David Bowman, Roman Saladino

Anthropomorphic language describing artificial intelligence (AI) is widespread in media, policy, and everyday discourse; so too are discussions of AI bad behavior, from hallucinations to inappropriate comments. How does humanizing language about AI shape moral judgments when AI behaves badly? Across four experiments (total N = 1,020), we tested whether lexical anthropomorphism (LA) primes shape judgments of AI moral character, behavior morality, and behavioral responsibility. Studies 1-3 tested interactions between anthropomorphic language and humanizing design cues (icons, names, self-referencing) in the context of amoral errors. Study 4 extended this to genuinely immoral AI behavior across seven moral-violation types. Results indicate humanizing language and design cues have little influence on moral judgments of misbehaving AI. Where effects emerged, high-anthropomorphic primes elevated perceptions of an AI's capacity for dishonesty. The type of moral violation observed was the strongest predictor of moral judgments, with harm and degradation violations producing the broadest negative character assessments. Prime drift, horn effects, and egoistic value orientations emerged as potentially important predictors of AI moral judgments.

Jaime Banks, Zhixin Li

The notion of machine companions has long been embedded in social-technological imaginaries. Recent advances in AI have moved those media musings into believable sociality manifested in interfaces, robotic bodies, and devices. Those machines are often referred to colloquially as "companions" yet there is little careful engagement of machine companionship (MC) as a formal concept or measured variable. This PRISMA-guided scoping review systematically samples, surveys, and synthesizes current scholarly works on MC (N = 71; 2017-2025), to that end. Works varied widely in considerations of MC according to guiding theories, dimensions of a-priori specified properties (subjectively positive, sustained over time, co-active, autotelic), and in measured concepts (with more than 50 distinct measured variables). WE ultimately offer a literature-guided definition of MC as an autotelic, coordinated connection between human and machine that unfolds over time and is subjectively positive.

Jaime Banks, Jianghui Li

Mind perception (MP) is a psychological phenomenon in which humans automatically infer that another entity has a mind and/or mental capacities, usually understood in two dimensions (perceived agency and experience capacities). Despite MP's centrality to many social processes, understanding how MP may function in humans' machine companionship relations is limited. This is in part due to reliance on self reports and the gap between automatic MP processes and more purposeful and norm governed expressions of MP. We here leverage MP signaling language to explore the relationship between MP and AI companionship in humans' natural language. We systematically collected discussions about companionship from AI dedicated Reddit forums and examined the cooccurrence of words (a) known to signal agentic and experiential MP and those induced from the data and (b) discussion topics related to AI companionship. Using inductive and deductive approaches, we identify a small set of linguistic indicators as reasonable markers of MP in human/AI chat, and some are linked to critical discussions of companion authenticity and philosophical and ethical imaginaries.