The research team, led by Vedant Tapiavala of Dartmouth College together with collaborators at Massachusetts General Hospital, Dartmouth’s Thayer School of Engineering, Indiana University, the Geisel School of Medicine, and The Bridge in New York City, conducted a secondary analysis of three focus groups held across three supportive housing facilities in New York City. Rather than surveying attitudes with questionnaires, the investigators collected qualitative data, transcribed the discussions, and analyzed them using the RADaR technique, a rapid and rigorous qualitative data analysis method developed for applied research. The approach allows researchers to move quickly from raw transcripts to structured themes while preserving the nuance of what participants actually said, an important consideration when the subject matter touches on trust, vulnerability, and lived experience.

The findings reveal a population that is neither naively enthusiastic nor reflexively hostile toward AI. Participants expressed strong concerns about data privacy and the potential misuse of their information, worries that are amplified for people whose psychiatric histories carry heavy social stigma. At the same time, they recognized real potential in AI tools to support medication reminders, mental health maintenance, and daily functioning. This duality, hope shadowed by caution, runs through the entire study and complicates the simple narratives that often dominate public discussion of chatbots and mental health. For this community, the question is not whether AI companions should exist, but under what conditions they can be trusted.

Trust, the study found, is not a fixed trait that users either bring to or withhold from a system. Instead, it is something that emerges from design choices. Participants reported that their confidence in AI increased when tools were personalized, human-centered, and co-developed with people who understand the lived experience of serious mental illness. That last condition may be the most consequential. A chatbot designed without input from people who have navigated psychosis, mania, or severe depression may miss the signals that matter most, or worse, respond in ways that feel dismissive or unsafe. Co-design, in which end users participate as partners rather than test subjects, emerged as a key factor shaping perceived trustworthiness.

The technical implications of this are significant. Modern conversational AI systems, particularly those built on large language models, are trained on broad datasets that reflect general human communication, not the specific communication patterns, needs, and sensitivities of people with serious mental illness. Personalization in this context means more than remembering a user’s name. It involves calibrating tone, anticipating cognitive and emotional states, respecting boundaries around sensitive topics, and integrating gracefully with clinical care teams. Participants in the study effectively described a set of design requirements that developers of mental health AI would do well to treat as specifications rather than suggestions.

Yet the study also documents a darker set of worries that the participants themselves raised: the fear of losing critical thinking and becoming overreliant on AI companions. This concern is especially poignant for a population where autonomy and self-efficacy are hard-won achievements. An AI that is always available, always agreeable, and always attentive could, in principle, substitute for human connection rather than supplement it. The researchers note that it is essential these technologies lessen, rather than deepen, feelings of loneliness, a distinction that sounds subtle but carries enormous weight for the design and deployment of companion AI in mental health care.

These concerns do not arise in a vacuum. The study’s reference list situates the work within a rapidly growing literature on the mental health risks of large language model chatbots, including recent scoping reviews on chatbot-related harms, analyses of AI-associated delusions and mechanisms of delusion co-creation, and debates in leading psychiatry journals about whether generative AI chatbots might increase psychosis risk. For people with schizophrenia spectrum disorders in particular, the possibility that an AI companion could reinforce distorted thinking is a clinical question of the first order. The study’s participants, by voicing their own concerns about overreliance, demonstrated a sophisticated awareness of these risks that contradicts stereotypes about people with serious mental illness being unable to engage critically with technology.

At the same time, the broader research landscape suggests why AI companions hold such appeal. Loneliness and social isolation are widespread among adults in the United States and are especially pronounced among people with serious mental illness, who face stigma, social withdrawal, and reduced lifespans compared with the general population. Some studies have even found that third-party evaluators perceive AI responses as more compassionate than those of expert humans, and systematic reviews of chatbots versus human healthcare professionals have examined empathy in patient care with mixed but intriguing results. Against that backdrop, a companion that offers unconditional availability and a consistent, nonjudgmental presence has an obvious emotional pull, particularly during the nights and weekends when human support is scarce.

The study’s methodology also deserves attention for what it models. By recruiting participants through supportive housing facilities and partnering with community organizations, the research team reached a population that digital health studies frequently overlook. People with serious mental illness have historically been excluded from technology design processes, sometimes on the assumption that they cannot use or evaluate complex tools. Earlier qualitative work on how patients with schizophrenia use the internet, and international consensus efforts on digital mental health for severe mental illness, have pushed back against that exclusion. This study extends that tradition by treating participants as experts in their own lives, capable of articulating precisely what would make an AI companion feel safe, useful, and trustworthy.

The path forward, the authors suggest, lies in using these lived-experience insights to guide the development of AI tools for people with serious mental illnesses. That means building privacy protections that address the specific fears participants raised, designing personalization that respects clinical complexity, involving peer specialists and people with lived experience throughout the development cycle, and building in safeguards against overreliance. It also means honest acknowledgment of the tensions involved: the same system that tells a lonely person someone cares can become a substitute for the human relationships that recovery ultimately depends on. As AI reshapes healthcare through clinical decision support, communication tools, and patient monitoring, this study offers a reminder that the people most affected by these technologies have clear views about how they should work, and that listening to them is not just ethical but essential to building systems that actually help.

Subject of Research: Perspectives of people with serious mental illness on artificial intelligence-based companions in mental health care

Article Title: Perspectives of People with Serious Mental Illness on Artificial Intelligence-Based Companions: “If no one told you I love you today, you have someone that cares for you.”

Article References: Tapiavala, V., Chilakapati, S. S., Moore, D., Morales, S., Kumaran, A., Heller, R., Werlin, J., & Fortuna, K. L. (2026). Perspectives of People with Serious Mental Illness on Artificial Intelligence-Based Companions: “If no one told you I love you today, you have someone that cares for you.”. Community Mental Health Journal. https://doi.org/10.1007/s10597-026-01732-4

Image Credits: AI Generated

DOI: 10.1007/s10597-026-01732-4

Keywords: artificial intelligence, serious mental illness, AI companions, loneliness, schizophrenia, bipolar disorder, major depressive disorder, co-design, data privacy, digital mental health, qualitative research, supportive housing

The study, authored by Brady Kennedy of Columbia University’s Department of Sociology, Cristian Capotescu of Columbia’s Trust Collaboratory, and Jennifer Laird of Lehman College, challenges the prevailing assumption that trust in smart city technologies can be engineered through better privacy protections, more reliable sensors and clearer communication. Drawing on a thematic synthesis of sociological research on trust alongside the literature on public participation, the authors contend that trust is not an attitude stored in the minds of individuals and measured by surveys, but a property of ongoing social relations between residents and the experts and institutions behind these initiatives.

Smart city initiatives typically combine several layers of technology: distributed sensors and connected devices grouped under the heading of the Internet of Things, which collect data from the built environment; information and communication technologies and cloud computing, which transmit and process that data at scale; and increasingly, artificial intelligence and algorithmic decision-making, which translate the resulting data into classifications and predictions for operational use. Stated aims range from optimizing public services and improving environmental sustainability to making infrastructure more accessible and cities safer and more livable.

Much existing research on trust in smart cities operates within a risk-benefit logic. Scholars have produced testable models simulating how mistrust hinders deployment, measured public attitudes through surveys, and proposed lists of trust characteristics such as privacy, reliability and fairness that designers can integrate into systems. The assumption is that as initiatives follow prescribed security parameters, privacy standards and governance norms, and communicate those choices transparently, public trust will follow. Kennedy and colleagues call this a technology-centric view that treats trust as flowing in one direction, with the public asked to extend it according to how experts adjust technical features.

In contrast, recent sociological scholarship, particularly from the field of science, technology and society, understands trust as inherently relational. In this account, trust emerges from interactions between trustors, the people doing the trusting; trustees, those in whom trust is placed; and trust objects, the specific focus of the relation at a given time and place. A smart city initiative may be the object of a trust relation, but it is never the trustee: residents extend or withhold trust with respect to the agency, vendor or partnership that designs and operates the system. Crucially, because uncertainty is what makes trust necessary in the first place, its conditions can never be fully engineered away through technical decisions.

The authors further draw on work describing trust as a verb, an active practice of trusting. Trusting is a skillful evaluative process in which residents scrutinize information and conduct rather than placing blind faith in experts. This carries a demand for both sides: the trustor must do the work of assessment, and the trustee must render itself answerable to that scrutiny. Because the trustee in these relations is an institution whose authority is itself at issue, answerability requires that decisions, and the power behind them, be made contestable. Notably, the authors argue that mistrust is not a failure of trust-building but internal to the same relational ties, playing an important role in promoting accountability and public engagement.

From this foundation, the study develops a typology of three participatory roles: the public as co-accessors, co-collectors and co-designers. Each role names the position residents occupy relative to smart city experts, and, because a relation cannot be specified from one side alone, also names what experts must place at stake. As co-accessors, residents shape, monitor and contest how data is gathered and used, while institutions accept the vulnerability of scrutiny. Examples range from static open data dashboards such as London’s DataStore to deeper arrangements like Barcelona’s New Data Deal, which re-envisions collected data as a public commons, and Manchester’s data governance practices, where residents and policymakers negotiate data gathering within structured engagements.

As co-collectors, residents directly produce data for smart city systems, through passive smartphone sensing or active crowdsourcing, while experts accept dependence on evidence they did not themselves produce. The MiraMap platform in Torino, Italy, allows residents to report and track urban problems, research showing it increased trust in government and technology alike. At the furthest extent, residents deploy their own sensors through systems such as HabitatMap AirSense and AirBox to generate environmental evidence independent of official accounts, converting mistrust into an instrument of accountability. As co-designers, finally, residents participate in shaping what is built, with experts relinquishing exclusive control. Iterative workshop approaches that visibly incorporate resident feedback have been shown in follow-up surveys to increase community trust in both the technology and the researchers, while game-based and virtual reality formats help include elderly and non-technical participants.

The framework carries pointed lessons for practitioners. Tokenism and consultation that leave residents feeling passively managed are not failures of technique but interactions in which no reciprocity was established; the mistrust that follows is a proportionate response, as illustrated by the urban data trust proposed for Sidewalk Toronto, constituted by the operator on the operator’s own terms, which failed to generate the mutuality on which trusting depends. The authors also caution that participatory approaches do not encounter a ready-made public but bring one into being, and that formats demanding time, mobility and technical confidence tend to reproduce existing inequalities, underrepresenting marginalized groups. Compensation and trusted community intermediaries can help widen who participates.

Ultimately, the authors argue, smart city professionals should stop treating participation as an instrument for raising approval scores and start treating it as the intentional structuring of reciprocal interaction in which trust, and mistrust, can both operate productively. There is no single modality for building trust, and each initiative will pose context-specific challenges. But without a relational understanding in which experts make themselves genuinely answerable, they warn, even well-intentioned future initiatives will face the same public backlash that has already derailed smart city projects from Toronto to San Diego.

Subject of Research: Trust-building through public participation in smart city initiatives

Article Title: Reframing participatory approaches to the smart city as opportunities for trust-building

Article References: Kennedy, B., Capotescu, C., & Laird, J. (2026). Reframing participatory approaches to the smart city as opportunities for trust-building. Discover Cities, 3(1), Article 177. https://doi.org/10.1007/s44327-026-00352-7

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00352-7

Keywords: smart cities, public trust, public participation, sociology of trust, STS, expertise, data governance, participatory design, citizen sensing, urban technology, co-design, mistrust