This work explores how increasingly capable AI agents may generate the perception of deeper relationships with users, especially as AI becomes more personalised and agentic.
It is argued that social scientists can address many of these limitations of Generative AI by creating open-source infrastructure for research on human behavior, not only to ensure broad access to high-quality research tools, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.
The Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR.
This study presents, for the first time in GBD, a quantification of the mean age at the time of suicide death, alongside comprehensive estimates of the burden of suicide throughout the world.
A taxonomy for risks and benefits of personalized LLMs is developed and the need for normative decisions on what are acceptable bounds of personalization is discussed, to enable users to benefit from personalized alignment while safeguarding against harmful impacts for individuals and society.
BLEnD, a hand-crafted benchmark designed to evaluate LLMs' everyday knowledge across diverse cultures and languages, is introduced and shows that LLMs perform better for cultures that are highly represented online, with a maximum 57.34% difference in GPT-4, the best-performing model, in the short-answer format.
Results from a series of nationally representative U.S. surveys of genAI use at work and at home suggest that work adoption of genAI has been faster than the personal computer (PC), and overall adoption has outpaced both PCs and the internet by an even wider margin.
This first attempt to describe major disparities in access to fertility care in the context of the global trend of decreasing growth in the world population, based on a narrative review of the existing literature is presented.
Findings highlight the need for public health interventions to address these social factors and improve health outcomes in older adults and identify longer follow-up, female sex, validated social network indices, adjustments for cognitive function, and study quality as significant predictors of mortality risks.
CVQA is constructed, a new Culturally-diverse multilingual Visual Question Answering benchmark, designed to cover a rich set of languages and cultures, where native speakers and cultural experts in the data collection process and can serve as a probing evaluation suite for assessing the cultural capability and bias of multimodal models.
A definition of DDOH is proposed and a conceptual model characterizing its influence on healthcare outcomes is provided that is implicit in the design of artificial intelligence systems, mobile phone applications, telemedicine, digital health literacy and other forms of digital technology.
How concepts of culture and context feature in design and development processes, including the methods, models, and content of digital health interventions are examined, with the aim of helping researchers to make informed decisions about how to approach cultural adaptation in digital health.
To accelerate an end to diabetes stigma and discrimination, an international multidisciplinary expert panel conducted rapid reviews and participated in a three-round Delphi survey process, achieving unanimous consensus on a pledge to end diabetes stigma and discrimination.
The findings reveal that self-efficacy plays a crucial role in shaping attitudes towards AI, with higher levels of self-efficacy associated with more positive attitudes and enhanced well-being and the need for culturally sensitive design and implementation of AI to ensure responsible development and implementation of AI for diverse populations.
By considering factors across intervention, staff, patient, and system levels, stakeholders can address challenges and leverage local strengths to enhance implementation success and reduce health disparities.
The findings suggest that clinicians should provide anticipatory guidance regarding social media use for young adolescents and their parents using longitudinal, cross-lagged structural equation panel models.
The findings demonstrate that LLMs can reproduce human-like behaviors, such as fairness, cooperation, and social norm adherence, while also introducing unique advantages such as cost efficiency, scalability, and ethical simplification.
The potential for improved health outcomes, heightened patient engagement, and the delivery of culturally relevant services within LMICs is highlighted, as well as innovative approaches and success stories in implementing patient-centered care.
While patients were generally supportive of AI-enabled health care facilities and recognized the potential of AI, they preferred explainable AI systems and physician-led decision-making and attitudes varied significantly by sociodemographic characteristics.
This review demonstrated that understanding intersectional characteristics (age, gender, disability, race, ethnicity, Indigenous identity and immigration status) and their interconnections is crucial for analyzing the dynamics of digital (in)equity and divide.
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