The focus on AI safety disasters ignores everyday harms

Consider what passes for success in technology policy. An AI company promises that its chatbot will not encourage suicide. A social media platform makes it more difficult for children to buy fentanyl. These are important protections, and they should have existed all along. But companies can clear these low bars while continuing to make products that leave young people dependent on simulated friendships or trapped in feeds built to keep them scrolling.

We have historically allowed the debate over technology to be defined by the worst thing a product might do, with remarkably little discussion of the insidious damage it does every day. As we transition into an era defined by powerful AI products, we should demand that the tech industry do more than merely address catastrophic harms. We should take measures to ensure that its products don’t erode how we think, learn, and interact with one another.

 A teenager who develops an unhealthy attachment to an AI companion may never appear in a lawsuit or a newspaper, except in the most egregious cases. Neither will the student who begins relying on a chatbot to write every paper, or the young person who measures their life against a social media feed engineered to provoke envy and keep them watching. A product can meet every narrow safety requirement and still leave our entire culture worse off in ways that don’t always make headlines.

A narrow definition of success defined around safety is politically convenient for the technology industry, and companies actively promote such definitions. Taking steps to prevent spectacular disasters is good for business: It can mean fewer lawsuits and less pressure for serious regulation. Confronting the everyday consequences of dependence would require something considerably more threatening to their bottom line. It might mean asking companies to add more friction to their products or actively limit engagement or features that lead to negative outcomes, such as addictive attention-hacking algorithms.

Technology giants have instead worked to shape legislation around the harms they are already under direct pressure to address. This year, for example, Google backed bills to regulate AI chatbots in several states. The legislation mandated basic protections against sexual content and promotion of self-harm that tech companies should have established regardless, but with broad carve-outs that could exempt some of the most popular services. Passing these bills allowed legislators to claim a safety win while efforts to promote stronger legislation—laws designed to prevent children from becoming emotionally dependent on AI—failed. This playbook worked in states like Nebraska, producing legislation heavily influenced by industry at the expense of bills that would have done more to hold companies accountable.

We have been through a version of this with social media. Globally, people who use the internet now spend an average of more than two hours a day on social media platforms—time that has been quietly taken from ordinary life and handed over to companies whose profits depend on keeping us there. No one, outside perhaps a few tech CEOs, wanted these algorithms to occupy such a large portion of our waking hours, and their cultural engineering can actually contribute to violence on a devastating scale. Meta’s platforms have been cited for contributing to the Rohingya genocide in Myanmar, anti-Muslim riots in Sri Lanka, and alleged calls for violence during Ethiopia’s Tigray war. The same incentives operate in the feeds that shape young Americans’ understanding of politics, their peers, and the wider world.

AI is extending this logic into parts of life that social media could influence only from a distance. It offers to write our thoughts for us and stand in for the people we might otherwise turn to. A third of teens now choose to discuss serious matters with AI companions instead of other people. MIT researchers found that young people using ChatGPT to write essays showed weaker brain connectivity during the task, and their evidence indicated that “cognitive debt” persisted even after the task was complete. There is plenty we still need to learn about the long-term effects. We already have reason to ask whether technology marketed as making us more capable is eroding the habits and relationships that help us develop capabilities in the first place.

Follow this way of thinking to its logical endpoint and you get a world of perfectly sanitized entertainment, with chatbots ready to spare us the difficulty of thinking or of dealing with another person’s feelings (or even our own). But anyone who has struggled to write something worth reading, or repair a damaged friendship, knows how much gets lost in that bargain. We build judgment through effort and friction, and we learn to live with other people by confronting the fact that they have needs and opinions of their own. 

We should demand safety as a minimum, yes—and then demand considerably more. Technology companies and school districts should have to show beyond a reasonable doubt that AI improves learning and critical thinking before making it a fixture in classrooms. Companies should be prohibited from designing companions that pretend to be conscious or profess love to users, and they should face independent scrutiny of how their systems affect relationships and people’s ability to think for themselves. The companies that have inserted their products into nearly every part of modern life have earned obligations far greater than avoiding the next terrible headline.

Sam Hiner is the cofounder and executive director of the Young People’s Alliance, where he coordinated a coalition of advocates to pass laws regulating AI companions in eight states in 2026—laws such as California’s SB1119. Sam graduated from the University of North Carolina atChapel Hill in 2025 and was named to the 2026 Forbes 30 Under 30 Social Impact list.

Emma Leiken is executive director of the Responsible Technology Youth Power Fund at Hopelab, which has raised more than $9 million for youth-led tech accountability work, and teaches technology policy at the Columbia School of International and Public Affairs. She was previously TikTok’s global head of youth safety and well-being policy and worked on responsible technology at Omidyar Network.

Correlation between hypothalamic-pituitary-adrenal function and clinical characteristics in the acute phase of first-episode drug-naive major depressive disorder

BackgroundFirst-episode drug-naïve depressive disorders have high heterogeneity in clinical features, and there is still room for in-depth exploration of the correlation with serum cortisol levels. This study aimed to explore the correlation between hypothalamic-pituitary-adrenal (HPA) function in the acute phase of first-episode drug-naïve major depressive disorder (MDD) and clinical features evaluated by the Hamilton Depression Rating Scale (HAMD-24) and the Zung Self-Rating Depression Scale (SDS).MethodsPatients with first-episode drug-naïve MDD in the acute phase meeting the DSM-V diagnostic criteria were selected as the study group, and healthy individuals matched for gender and age were recruited as the control group. Serum cortisol levels were measured in both groups, and patients in the MDD group were evaluated using HAMD-24 and SDS.ResultsThe serum cortisol level in the MDD group(307.03 ± 77.80 nmol/L) was significantly higher than that in the healthy control group(261.13 ± 58.15 nmol/L) (Z-Value = -3.539, P < 0.05). The serum cortisol level showed a positive correlation trend (ρ > 0) with the anxiety factor, cognitive impairment factor, retardation factor, despair factor, total HAMD score, and SDS score, with statistical significance (p < 0.05). It showed a negative correlation trend (ρ < 0) with the sleep factor, without statistical significance (p > 0.05). It also showed a positive correlation trend (ρ > 0) with the weight factor and diurnal variation factor, without statistical significance (p > 0.05).ConclusionA significant increase in serum cortisol level can serve as one of the biological indicators for predicting the core symptom clusters and disease severity of MDD. Regulation of The HPA axis cortisol pathway may have potential value in alleviating MDD symptoms.

Multiscale EEG network alterations in adolescents with depressive symptoms: static–dynamic connectivity, topological features, and exploratory scalp-level network hubs

BackgroundAdolescence is a critical period for brain network development and a vulnerable stage for the emergence of depressive symptoms Although altered brain connectivity has been reported in depression, the multiscale organization of resting-state EEG networks in adolescents with depressive symptoms (ADS) remains insufficiently understood. In particular, conventional network analyses are often constrained by fixed thresholding strategies and single-level observations, which may obscure scale-specific and topology-related abnormalities.MethodsEyes-closed resting-state EEG data were collected from 96 adolescents with depressive symptoms and 154 healthy controls. A data-driven adaptive thresholding strategy was used to construct subject-specific brain functional networks. Static functional connectivity, dynamic functional connectivity, and higher-order topological features were analyzed across macro-, meso-, and micro-scale levels. A multidimensional abnormality convergence matrix was further constructed to explore the spatial correspondence between network abnormalities and exploratory scalp-level neuromodulation hubs.ResultsADS showed scale-dependent abnormalities in brain network organization. At the macro- and meso-scale levels, abnormalities were mainly characterized by theta-band static hyperconnectivity, particularly involving central-region networks, together with reduced dynamic transition flexibility involving prefrontal systems. Micro-scale analysis further localized these alterations to specific connections and nodes. Static connectivity abnormalities were mainly observed along the midline prefrontal–central axis, including Fp1/Fz–FCz connections, whereas dynamic instability was mainly found in right prefrontal–occipital pathways, including Fp2/F4–Oz connections. Higher-order topological analysis indicated vulnerability of right prefrontal nodes, particularly Fp2 and F4. The convergence matrix showed spatial overlap between abnormal network features and the approximate scalp projections of prefrontal neuromodulation sites, while also identifying FCz and Oz as additional nodes that may warrant further investigation.ConclusionThis study provides a multiscale EEG network characterization of adolescents with depressive symptoms. The findings suggest that ADS-related network alterations may involve coexisting compensatory connectivity changes, reduced dynamic flexibility, and focal topological vulnerability. These results may help refine network-level features and provide hypothesis-generating computational references for future studies exploring individualized neuromodulation strategies in adolescents with depressive symptoms.

Predicting behavioral escalation in students with autism using physiological and movement data

IntroductionBehavioral escalation in students with Autism Spectrum Disorder (ASD) presents significant challenges in educational and community settings, often leading to reactive interventions after behaviors become observable. This study investigated whether physiological and movement data obtained from wearable biometric sensors could retrospectively identify precursors of escalation and inform proactive support strategies.MethodsPhysiological and movement data were collected from 30 students with ASD in naturalistic classroom environments using wearable sensors. Measures included heart rate variability (HRV), galvanic skin response (GSR), skin temperature, and accelerometer data. Behavioral observations were used to identify escalation events, and machine learning models, including Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks, were trained on previously collected physiological and behavioral data to classify pre-escalation states. Model performance was evaluated retrospectively rather than through real-time deployment.ResultsAmong the models tested, the LSTM network achieved the highest classification accuracy (85.2%) in identifying pre-escalation states. Physiological changes associated with escalation were detectable approximately 2 to 5 minutes before observable behavioral manifestations. HRV and GSR emerged as the strongest predictive features, while inclusion of accelerometer data further improved classification performance. These findings suggest that measurable physiological patterns precede overt behavioral escalation in students with ASD.DiscussionThe results support the feasibility of using wearable biometric data and machine learning approaches to retrospectively identify antecedent physiological markers of behavioral escalation. Interpreted through frameworks drawn from Applied Behavior Analysis (ABA) and Cognitive Behavioral Therapy (CBT), these physiological signals may function as both antecedents to challenging behavior and indicators of internal emotional or regulatory states. Although real-time implementation was not evaluated, the findings provide a foundation for future predictive monitoring systems designed to facilitate earlier intervention, reduce reliance on reactive or punitive responses, and promote more inclusive and emotionally responsive educational environments for neurodiverse learners.

Human-in-the-Loop Suicide Risk Detection and Clinical Escalation in a Generative AI Mental Health Chatbot: Prospective Implementation Study

<strong>Background:</strong> Mental illness is a leading cause of disability worldwide, affecting more than 1 billion people, and suicide accounts for 720,000-800,000 deaths annually. Although AI-native mental health chatbots have the potential to help address the substantial gap between treatment needs and workforce capacity, their rapid proliferation has outpaced the development of scalable procedures for identifying and responding to suicidal ideation. Although recommendations call for human oversight to support safety monitoring and intervention in AI-based care, research on human clinical oversight and crisis-response procedures remains limited. <strong>Objective:</strong> This study aims to implement a human-in-the-loop (HITL) protocol for suicide risk screening, detection, and clinical escalation in a generative AI (GenAI) mental health chatbot. <strong>Methods:</strong> In this implementation study of a HITL suicide risk detection and escalation protocol, adults were recruited online to participate in a single-arm trial of an AI-native mental health chatbot. Participants completed baseline screening using the 9-item Patient Health Questionnaire (PHQ-9). In phase 1, participants who endorsed suicidal ideation were referred to a human clinician for further assessment, safety planning, and provision of referral resources, as appropriate. In phase 2, escalation was triggered by either PHQ-9 responses or the detection of spontaneous, implicit, or explicit disclosures of suicidality during chatbot interactions. In both phases, the Ask Suicide-Screening Questions guided clinician-led assessment and risk stratification. Outcomes included the prevalence of suicidal ideation, the rate of successful clinician outreach, the time from risk detection to initiation of clinician outreach, and the results of risk stratification. <strong>Results:</strong> In phase 1, 624 participants completed suicide risk screening, of whom 81 (13%) endorsed PHQ-9 item 9 (score ≥1). Clinician outreach was initiated, on average, 11.2 minutes (SD 13.2 minutes; range 1-54 minutes) after the alert, and 25 of the 81 (31%) participants were successfully reached. In phase 2, 650 participants completed screening, of whom 133 (20.5%) screened positive at baseline. During the 30-day postbaseline observation period, 284 risk-alert events were generated among 172 (26.5%) participants, including 50 conversational suicidal ideation alerts. Clinicians successfully reached 73 of the 172 (42.4%) participants. The mean time from risk detection to initiation of clinician outreach was 11.5 minutes (SD 11.7 minutes; range 1-58 minutes). <strong>Conclusions:</strong> Proactive, universal suicide risk screening and conversational detection using a HITL protocol can be operationally implemented in a GenAI mental health chatbot. A subset of participants who were successfully reached by a human clinician described the approach as acceptable and helpful. Given that clinical contact and assessment were not completed for a substantial proportion of alerted participants, further research is needed to optimize assessment and follow-up strategies for individuals who cannot be reached and to characterize the impact of these procedures on clinical outcomes. Nevertheless, the safety procedures described herein provide a framework for developing and refining HITL safety responses and protocols for purpose-built, AI-native mental health chatbots.

Meta Mesh Ontology for Cross-Domain Mental Health Knowledge Integration: Development and Evaluation Study

Background: Psychiatric diagnostic practice faces persistent challenges in adequately capturing the complexity, heterogeneity, and dynamic nature of mental disorders. Conventional categorical classification systems such as the and the rely on rigid symptom-based criteria that often fail to represent multifactorial etiologies, dimensional symptom profiles, and high rates of comorbidity. Although dimensional models, domain-specific ontologies, and alternative taxonomies have been developed to address these limitations, they are typically applied in isolation, resulting in fragmented knowledge representations and limited interoperability. This lack of semantic integration hampers data harmonization and cross-domain analysis. Objective: The objective of this work is to develop and present the Meta Mesh Ontology (MMO) and its Core Domain Framework (CDF) as an integrative approach for mapping heterogeneous psychiatric knowledge bases and evaluating ontology structure, logical consistency, and specified retrieval and inference behavior. Methods: We designed a modular ontology architecture grounded in the Basic Formal Ontology to ensure structural and semantic consistency across biological, psychological, and social domains. Existing psychiatric frameworks and ontologies—including dimensional models, diagnostic taxonomies, and socioenvironmental knowledge systems—were systematically analyzed and represented through domain modules using a documented alignment and mapping procedure. The MMO was implemented using formal semantic web standards, including the Web Ontology Language and the Resource Description Framework (RDF), and aligned with established biomedical ontologies and informatics standards to support explicit mappings and modular extensions across domains. Evaluation used 2 MMO and CDF metric sets and 10 module metric sets to quantify ontology size, semantic alignments, external integration, and dataset readiness. We also assessed Web Ontology Language 2 Description Logic (OWL 2 DL) conformance, logical consistency, and 11 competency questions (including 1 entailment test) and 4 negative controls, in an outpatient scenario with 50 variables. Results: The MMO provides a shared semantic framework for representing links among heterogeneous psychiatric knowledge sources. Its modular design allows domain-specific subontologies to be incorporated under a common architecture. MMO and CDF comprised 2238 triples, 208 classes, 28 properties, 3 individuals, and 103 alignments (100 external) across 5 frameworks. The 10 modules added 967 triples and 171 alignments (116 external), with 21 integrations and complete label coverage (54/54 resources). The ontology resources conformed to OWL 2 DL; HermiT (Information Systems Group) detected no logical inconsistency, and all 11 competency questions and 4 negative controls produced the expected results. All 50 variables were linked to at least 1 CDF anchor, and 47 had an explicit Simple Knowledge Organization System (SKOS) mapping. Conclusions: The MMO provides a modular foundation for representing links among diverse psychiatric knowledge systems and data modalities. The current evaluation characterizes its structure, mappings, and behavior in the specified tests. Further research should assess mapping validity, governed clinical data, operational performance, and clinical utility.
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Comparison of short-term peripheral nerve stimulation, single-target supraorbital nerve pulsed radiofrequency, and dual-target supraorbital nerve–stellate ganglion pulsed radiofrequency for herpes zoster ophthalmicus–associated pain: a retrospective cohort study

ObjectiveTo compare the clinical outcomes of short-term peripheral nerve stimulation (st-PNS), supraorbital nerve pulsed radiofrequency (SON-PRF), and combined supraorbital nerve and stellate ganglion pulsed radiofrequency (SON + SG-PRF) for herpes zoster ophthalmicus (HZO)-associated pain.MethodsThis retrospective study included patients with HZO-associated pain who underwent st-PNS, SON-PRF, or SON + SG-PRF between January 2023 and January 2026. The primary outcome was the longitudinal change in Numerical Rating Scale (NRS) scores over 24 weeks. Secondary outcomes included the clinical response rate, anxiety (GAD-7), depression (PHQ-9), sleep quality (PSQI), and treatment-related adverse events. The incidence of postherpetic neuralgia (PHN) was assessed as an exploratory outcome only in patients with a disease duration of ≤90 days at treatment. Longitudinal outcomes were analyzed using baseline-adjusted linear mixed-effects models, with propensity score weighting used for sensitivity analyses.ResultsA total of 269 patients were included: 84 in the st-PNS group, 96 in the SON-PRF group, and 89 in the SON + SG-PRF group. NRS scores decreased over follow-up in all three groups. In the primary and both propensity score-weighted analyses, the overall NRS trajectory was lower with st-PNS than with SON-PRF, whereas no significant difference was observed between st-PNS and SON + SG-PRF. The overall GAD-7 trajectory was lower with SON + SG-PRF than with SON-PRF, and the overall PSQI trajectory was lower with SON + SG-PRF than with either st-PNS or SON-PRF; these differences remained statistically significant in both weighted analyses. The overall trajectories of depressive symptoms did not differ significantly among the groups. Exploratory analysis did not establish a difference in PHN risk among the three groups. No serious treatment-related adverse events occurred during follow-up.ConclusionTo our knowledge, this study is the first to compare multidimensional clinical outcomes of st-PNS, SON-PRF, and SON + SG-PRF for HZO-associated pain within the same cohort. st-PNS showed a relatively consistent analgesic benefit over SON-PRF, whereas SON + SG-PRF also provided analgesia and showed potential benefits for anxiety and sleep outcomes. These findings require confirmation in prospective studies.