Why Most Resist AI Companions

De Freitas, J., et al. (2025).

(Working Paper No. 25–030).

Here is a link.

Abstract

Chatbots are now able to form emotional relationships with people and alleviate loneliness—a growing public health concern. Behavioral research provides little insight into whether everyday people are likely to use these applications and why. We address this question by focusing on the context of “AI companion” applications, designed to provide people with synthetic interaction partners. Study 1 shows that people believe AI companions are more capable than human companions in advertised respects relevant to relationships (being more available and nonjudgmental). Even so, they view them as incapable of realizing the underlying values of relationships, like mutual caring, judging them as not ‘true’ relationships. Study 2 provides further insight into this belief: people believe relationships with AI companions are one-sided

(rather than mutual), because they see AI as incapable of understanding and feeling emotion. Study 3 finds that actually interacting with an AI companion increases acceptance by changing beliefs about the AI’s advertised capabilities, but not about its ability to achieve the true values of relationships, demonstrating the resilience of this belief against intervention. In short, despite the potential loneliness-reducing benefits of AI companions, we uncover fundamental psychological barriers to adoption, suggesting these benefits will not be easily realized.

Here are some thoughts:

The research explores why people remain reluctant to adopt AI companions, despite the growing public health crisis of loneliness and the promise that AI might offer support. Through a series of studies, the authors identify deep-seated psychological barriers to embracing AI as a substitute or supplement for human connection. Specifically, people tend to view AI companions as fundamentally incapable of embodying the core features of meaningful relationships—such as mutual care, genuine emotional understanding, and shared experiences. While participants often acknowledged some of the practical benefits of AI companionship, such as constant availability and non-judgmental interaction, they consistently doubted that AI could offer authentic or reciprocal relationships. Even when people interacted directly with AI systems, their impressions of the AI’s functional abilities improved, but their skepticism around the emotional and relational authenticity of AI companions remained firmly in place. These findings suggest that the resistance is not merely technological or unfamiliarity-based, but rooted in beliefs about what makes relationships "real."

For psychologists, this research is particularly important because it sheds light on how people conceptualize emotional connection, authenticity, and support—core concerns in both clinical and social psychology. As mental health professionals increasingly confront issues of social isolation, understanding the limitations of AI in replicating genuine human connection is critical. Psychologists might be tempted to view AI companions as possible interventions for loneliness, especially for individuals who are socially isolated or homebound. However, this paper underscores that unless these deep psychological barriers are acknowledged and addressed, such tools may be met with resistance or prove insufficient in fulfilling emotional needs. Furthermore, the study contributes to a broader understanding of human-technology relationships, offering insights into how people emotionally and cognitively differentiate between human and artificial agents. This knowledge is crucial for designing future interventions, therapeutic tools, and technologies that are sensitive to the human need for authenticity, reciprocity, and emotional depth in relationships.

The human black-box: The illusion of understanding human better than algorithmic decision-making

Bonezzi, A., Ostinelli, M., & Melzner, J. (2022). 

Journal of Experimental Psychology: General.

Abstract

As algorithms increasingly replace human decision-makers, concerns have been voiced about the black-box nature of algorithmic decision-making. These concerns raise an apparent paradox. In many cases, human decision-makers are just as much of a black-box as the algorithms that are meant to replace them. Yet, the inscrutability of human decision-making seems to raise fewer concerns. We suggest that one of the reasons for this paradox is that people foster an illusion of understanding human better than algorithmic decision-making, when in fact, both are black-boxes. We further propose that this occurs, at least in part, because people project their own intuitive understanding of a decision-making process more onto other humans than onto algorithms, and as a result, believe that they understand human better than algorithmic decision-making, when in fact, this is merely an illusion.

General Discussion

Our work contributes to prior literature in two ways. First, it bridges two streams of research that have thus far been considered in isolation: IOED (Illusion of Explanatory Depth) (Rozenblit & Keil, 2002) and projection (Krueger,1998). IOED has mostly been documented for mechanical devices and natural phenomena and has been attributed to people confusing a superficial understanding of what something does for how it does it (Keil, 2003). Our research unveils a previously unexplored driver ofIOED, namely, the tendency to project one’s own cognitions on to others, and in so doing extends the scope of IOED to human deci-sion-making. Second, our work contributes to the literature on clinical versus statistical judgments (Meehl, 1954). Previous research shows that people tend to trust humans more than algorithms (Dietvorst et al., 2015). Among the many reasons for this phenomenon (see Grove & Meehl, 1996), one is that people do not understand how algorithms work (Yeomans et al., 2019). Our research suggests that people’s distrust toward algorithms may stem not only from alack of understanding how algorithms work but also from an illusion of understanding how their human counterparts operate.

Our work can be extended by exploring other consequences and psychological processes associated with the illusion of understand-ing humans better than algorithms. As for consequences, more research is needed to explore how illusory understanding affects trust in humans versus algorithms. Our work suggests that the illusion of understanding humans more than algorithms can yield greater trust in decisions made by humans. Yet, to the extent that such an illusion stems from a projection mechanism, it might also lead to favoring algorithms over humans, depending on the underly-ing introspections. Because people’s introspections can be fraught with biases and idiosyncrasies they might not even be aware of (Nisbett & Wilson, 1977;Wilson, 2004), people might erroneously project these same biases and idiosyncrasies more onto other humans than onto algorithms and consequently trust those humans less than algorithms. To illustrate, one might expect a recruiter to favor people of the same gender or ethnic background just because one may be inclined to do so. In these circumstances, the illusion to understand humans better than algorithms might yield greater trust in algorithmic than human decisions (Bonezzi & Ostinelli, 2021).

A pdf of the research is here.