The core of their argument is deceptively simple: AI does not merely respond to existing challenges in long-term care, it actively reshapes them. By determining which data are collected, which risks are classified, and which outcomes are prioritized, AI systems influence how care needs are understood and addressed in the first place. Ethical risks, the authors argue, arise not only from technical performance failures but from something far subtler: the possibility that structural and relational problems in care systems are reframed as individual conditions that can be monitored and managed through technology. When loneliness, neglect, or inadequate staffing become data points on a dashboard, the underlying causes can escape scrutiny entirely.

These risks operate at several interconnected levels, the authors explain. At the individual and relational level, AI may affect autonomy, dignity, privacy, consent, and the relationships between older adults and their caregivers. AI-enabled systems can collect and process sensitive data without sufficient transparency about how that information is used, protected, or retained. Excessive reliance on AI may also weaken the human connection and reciprocity that lie at the heart of care itself. At the level of social justice, biased data, opaque systems, and weak accountability may deepen existing inequalities, particularly for older adults living with frailty, socioeconomic disadvantage, or limited AI literacy. And at the environmental level, scaling data-intensive AI carries material costs through energy consumption, water use, mineral extraction, and the expansion of data infrastructures.

Perhaps the most provocative claim concerns the cultural and epistemic level. Categories such as dependency, risk, efficiency, and quality of care, the authors stress, are not neutral descriptors. They reflect assumptions shaped by medical, administrative, commercial, and policy institutions, and those assumptions can become embedded in datasets, classification systems, platform design, and performance indicators. Decisions about what data to collect and which outcomes to optimize therefore encode particular ideas about normal aging, desirable behavior, appropriate care, and where responsibility for care should lie. The authors warn that these embedded assumptions may reinforce ageist and deficit-oriented representations of older people while marginalizing diverse experiences of aging and social relations. In this process, older adults risk being reduced to data subjects or measurable profiles, while their identities, relationships, life histories, and social belonging receive less and less attention.

To make this concrete, the authors offer a hypothetical scenario that reads like a cautionary tale for care facilities everywhere. Imagine an AI-powered chatbot introduced to reduce loneliness among residents of a long-term care home. In an underfunded facility facing chronic workforce pressures, staff may come to rely on the chatbot to monitor residents’ emotional states, flag signs of social isolation, and initiate conversations. The facility may then treat the technology as a substitute for investment in staffing, communal activities, and sustained human relationships. The system itself may rest on datasets that reflect narrow assumptions about normal social interaction, potentially misinterpreting culturally diverse forms of communication and connection. Its collection of personal data creates privacy and governance risks, while the computing infrastructure behind it carries environmental costs. Loneliness, in this scenario, has been reframed as an individual condition to be detected and technologically managed, while the organizational conditions that produced it remain untouched.

Existing regulation, the authors acknowledge, has made real progress. The European Union’s AI Act has established a risk-based approach to governing artificial intelligence, and China has been building an expanding framework for AI industry standards and technical standardization. These governance frameworks provide important safeguards. Yet compliance-oriented governance, Xu and Teerawichitchainan argue, tends to focus on the capabilities and risks of individual AI systems, framing responsibility mainly in terms of technical compliance rather than institutional responsibility for staffing, social infrastructure, and equitable care. Such approaches may leave unexamined the most important questions of all: why particular AI systems are adopted in the first place, which problems they are expected to solve, and how organizational arrangements, power relations, and sociocultural assumptions shape their use.

The authors’ proposed remedy draws on systems thinking, a framework pioneered by researcher Donella Meadows, whose influential work on leverage points described the places where intervention can alter how an entire system behaves. Systems thinking understands complex challenges as outcomes of dynamic relationships among technical systems, organizational arrangements, professional practices, and societal values. The key insight is that changes to technical parameters, such as improving model accuracy or tightening consent procedures, may reduce particular risks but leave the system’s underlying goals and structures intact. Deeper interventions instead seek to change how problems are defined, whose knowledge carries authority, what organizations are designed to achieve, and who holds the power to redirect the system. Applied to AI-enabled long-term care, this means that the most powerful levers are not in the code but in the context surrounding it.

From this perspective, the authors identify four domains in which deep leverage points may operate. The first encompasses societal paradigms and value systems at the level of policy and public discourse, which define what counts as aging well, technological progress, and quality of care. The second concerns epistemic authority in knowledge production and technical design, determining whose knowledge is used to define care needs, relevant data, and desirable AI outputs. The third involves systemic goals and models of AI-enabled care at the organizational level, which determine whether AI strengthens relational care or primarily advances efficiency, surveillance, and risk management. The fourth addresses distributed decision-making power and user agency, asking whether affected groups can shape, contest, modify, or discontinue the AI systems that touch their lives.

Each domain suggests a corresponding intervention pathway. At the policy level, the authors argue, ethical AI governance requires challenging the assumption that care challenges are best addressed through greater prediction, automation, and individual risk management. Quality of care could instead be defined in terms of dignity, reciprocity, inclusion, sustainability, and opportunities for meaningful social connection. In knowledge production, participation should extend well beyond token consultation: older adults, family caregivers, care workers, and community members should have a meaningful role in defining the problems to be addressed, determining which data are relevant, prioritizing outcomes, and deciding whether an AI application is needed at all. At the organizational level, procurement, funding, and evaluation arrangements should reward uses of AI that strengthen relational care and support the workforce, while facilities should assess whether AI applications redirect resources away from human care, intensify surveillance, or transfer institutional responsibilities onto residents and caregivers. Finally, affected groups need practical mechanisms to question, modify, suspend, or discontinue AI applications.

The article’s ultimate message is both a warning and an invitation. AI is transforming not only how long-term care is delivered but how care needs, social problems, and desirable outcomes are understood. Governance should therefore look beyond whether individual applications are safe or compliant and examine the care contexts into which they are introduced. This demands ongoing multidisciplinary learning about how workforce conditions, organizational priorities, social relationships, and inequalities shape AI implementation, and how AI, in turn, reshapes those conditions. Given the limited evidence on long-term effects, the authors conclude, governance should remain precautionary and adaptive. Most importantly, AI should never be allowed to reframe structural and relational problems as individual conditions to be technologically managed, particularly when doing so may reproduce underinvestment, weaken care relationships, or leave inadequate social infrastructure unaddressed. The future of ethical AI in elder care, in other words, depends less on smarter machines than on wiser choices about what we ask those machines to do.

Subject of Research: Ethical and responsible governance of artificial intelligence in long-term care for older adults

Article Title: Deep leverage points for ethical and responsible AI in long-term care

Article References: Xu, W., & Teerawichitchainan, B. (2026). Deep leverage points for ethical and responsible AI in long-term care. PLOS Aging and Health, 1(3), e0000041. https://doi.org/10.1371/journal.page.0000041

Image Credits: AI Generated

DOI: 10.1371/journal.page.0000041

Keywords: artificial intelligence, long-term care, aging, AI ethics, systems thinking, leverage points, elder care, AI governance, care workforce, ageism, loneliness, health policy

The research arrives at a moment of sharp inflection. According to data cited by the study’s authors, AI adoption across the health care sector rose from 5.9 percent in 2023 to 8.3 percent in 2025, with a 481.5 percent acceleration in the rate of adoption after late 2024. Home care is following the broader curve. AI-enabled wearable devices and ambient in-home sensor systems now track movement, sleep and vital signs to flag early clinical changes. Software platforms can automatically adjust caregiver schedules in real time based on patient acuity and geographic proximity, generate individualized care plans from aggregated patient data, and act as virtual assistants guiding patients through symptom reporting and communication with care teams. Yet the study’s authors note that almost nothing has been known about how the workforce at the center of this transformation perceives these systems.

The context makes that gap consequential. The United States faces a growing home care crisis as its population ages and more older adults experience complex medical needs and cognitive decline while wishing to remain at home. The home health aides and attendants who make aging in place possible are predominantly women from racial and ethnic minority backgrounds, earn low wages, receive insufficient training and recognition, are often undervalued by the health care system, and frequently work in isolation in patients’ homes with limited oversight or peer connection. Persistent workforce shortages and rising demand have made AI an attractive proposition for agencies and investors, but the study’s authors argue that integration is poised to carry both intended and unintended consequences for the relational, hands-on core of the work.

To capture stakeholder perspectives, researchers from Cornell Tech, Weill Cornell Medicine and Cornell University conducted semi-structured interviews between July 22, 2024 and April 18, 2025. Using purposive and snowball sampling, they recruited 43 participants across five groups: 11 home health aides, 10 home care agency leaders and staff, 14 worker advocates from unions and labor organizations, 5 clinicians with home care experience, and 3 technology company leaders and staff. Among the 36 participants with demographic data, the mean age was 44.6 years, 63.9 percent identified as women, 97.2 percent had completed at least some college, and 44.4 percent reported no or low knowledge of AI. Interviews lasted roughly an hour and were conducted over video conferencing, with all procedures approved by the Cornell University Institutional Review Board.

A distinctive methodological feature was the use of fictional vignettes grounded in real, commercially available AI products for home care. Each participant was assigned two of three scenarios: an AI tool to facilitate matching between aides and patients, a system to monitor aide-patient interactions in the home, and a tool to improve care coordination tasks. The vignettes were designed to give participants—who varied widely in technical familiarity—a consistent, realistic basis for discussion, and to encourage balanced reflection on benefits, risks and tensions. Technology company participants, given their deep familiarity with AI, instead answered questions about their own products. Interviews concluded with broader questions about governance and control.

The analysis followed a rigorous three-stage qualitative process: structural coding of high-level topics, inductive generation of sub-codes—223 in total—and thematic analysis, with transcripts coded in ATLAS.ti and transcription performed using a locally run open-source AI tool. Four major themes emerged. The first concerned benefits: participants saw AI improving documentation and symptom capture in the home, breaking down information barriers between different caregivers so that families and clinicians stay on the same page, strengthening aide engagement and retention through better job matches, and delivering efficiency gains for agencies and even for advocacy organizations answering routine member questions about contracts and benefits.

The second theme was darker. Participants, particularly clinicians, worried that AI could dehumanize care and corrode trust between providers and care recipients, with one physician imagining a world in which people are supposed to trust AI and therefore distrust each other. Many feared that AI would shift administrative labor onto aides, who would spend more time filling out forms on tablets and less time in direct human contact with clients. Agency staff and worker advocates emphasized that AI systems require large volumes of manually entered and maintained data, and that aides and agency staff would bear primary responsibility for keeping that information accurate, current and eventually removed—a burden one agency participant, drawing on a prior non-AI technology rollout, described as requiring hundreds of hours of manual input.

Participants also warned that these added burdens could worsen workforce shortages in an already strained labor market by stripping out the relational aspects of the job that keep people in it. Some noted that AI investment would flow most powerfully where incentives favor maximizing profits and minimizing wages, raising the specter of wage suppression. A third theme centered on data responsibilities, privacy and AI literacy: technology participants conceded that a system is only as good as the information put into it, clinicians questioned surveillance in the home and called for opt-out options, and aides themselves described a stark lack of agency—if the agency decides to put AI in a patient’s home, one aide said, the aide cannot say no. Limited AI understanding among aides, advocates added, could hinder their ability to advocate for themselves in contract negotiations.

The fourth theme carried a note of urgency and opportunity. Because AI adoption in home care is still nascent, participants saw a closing window to establish operational guidelines and labor protections before the technologies become entrenched. Labor organizations could embed AI provisions in contracts and educate their members, though several acknowledged their organizations lack internal technical expertise and may need to retool. Nearly all participants wanted clear governance and regulatory frameworks ensuring that care recipients, family members and the full care team have input into design and oversight. The authors note that while home care is regulated under HIPAA and Electronic Visit Verification requirements, AI-specific legislation has yet to emerge despite recommendations from the World Health Organization.

The study’s limitations are acknowledged by its authors: it was a small-scale qualitative study concentrated in urban, largely northeastern U.S. settings, so generalizability to rural or non-U.S. contexts remains unknown; the vignettes may have shaped participants’ views; and the perspectives of older adults and family caregivers themselves were not captured, representing a key direction for future research alongside ethnographic studies of real deployments. Still, the conclusions are pointed. AI tools may improve patient care, documentation and workforce engagement, but they risk eroding patient-provider relationships, increasing burdens on a strained workforce, and raising unresolved questions about data quality and privacy. Because adoption is early, the authors argue, stakeholders have a critical window to build inclusive governance frameworks centered on the aides and patients most affected—so that AI supports, rather than undermines, care quality and workforce sustainability in one of health care’s highest-stakes settings.

Subject of Research: Stakeholder perspectives on the benefits, risks and governance of artificial intelligence in home care work for older adults

Article Title: Understanding Key Stakeholders’ Perspectives Towards Artificial Intelligence in Home Care Work

Article References: Solano-Kamaiko, I. R., Dicpinigaitis, M., Tan, M., Avgar, A., Vashistha, A., Dell, N., & Sterling, M. R. (2026). Understanding Key Stakeholders’ Perspectives Towards Artificial Intelligence in Home Care Work. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10782-z

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10782-z

Keywords: artificial intelligence, home care, home health aides, qualitative research, health care workforce, AI governance, data privacy, labor unions, elder care, health services research, surveillance, workforce shortages