Hasil untuk "Men"

M. Marmot, G. Rose, M. Shipley et al.

D. O'leary, J. Polak, R. Kronmal et al.

D. Siscovick, N. Weiss, R. Fletcher et al.

J. S. Tenover

C. Osmond, D. J. Barker, P. Winter et al.

R. Tolman

A. Mosterd, A. Hoes, M. D. Bruyne et al.

J. M. Bailey, Kenneth J. Zucker

H. Weiss, M. Quigley, R. Hayes

M. Smith, F. Mcgovern, A. Zietman et al.

D. Felsenberg, A. Silman, M. Lunt et al.

D. Buss, T. Shackelford

Allen J. Taylor, Jody Bindeman, I. Feuerstein et al.

D. Laaksonen, H. Lakka, J. Salonen et al.

Laura J. Kray, Leigh Thompson, A. Galinsky

Michàlle E. Mor Barak, D. Cherin, Sherry L. Berkman

S. Lindau, Natalia Gavrilova

Objectives To examine the relation between health and several dimensions of sexuality and to estimate years of sexually active life across sex and health groups in middle aged and older adults. Design Cross sectional study. Setting Two samples representative of the US population: MIDUS (the national survey of midlife development in the United States, 1995-6) and NSHAP (the national social life, health and ageing project, 2005-6). Participants 3032 adults aged 25 to 74 (1561 women, 1471 men) from the midlife cohort (MIDUS) and 3005 adults aged 57 to 85 (1550 women, 1455 men) from the later life cohort (NSHAP). Main outcome measures Sexual activity, quality of sexual life, interest in sex, and average remaining years of sexually active life, referred to as sexually active life expectancy. Results Overall, men were more likely than women to be sexually active, report a good quality sex life, and be interested in sex. These gender differences increased with age and were greatest among the 75 to 85 year old group: 38.9% of men compared with 16.8% of women were sexually active, 70.8% versus 50.9% of those who were sexually active had a good quality sex life, and 41.2% versus 11.4% were interested in sex. Men and women reporting very good or excellent health were more likely to be sexually active compared with their peers in poor or fair health: age adjusted odds ratio 2.2 (P<0.01) for men and 1.6 (P<0.05) for women in the midlife study and 4.6 (P<0.001) for men and 2.8 (P<0.001) for women in the later life study. Among sexually active people, good health was also significantly associated with frequent sex (once or more weekly) in men (adjusted odds ratio 1.6 to 2.1), with a good quality sex life among men and women in the midlife cohort (adjusted odds ratio 1.7), and with interest in sex. People in very good or excellent health were 1.5 to 1.8 times more likely to report an interest in sex than those in poorer health. At age 30, sexually active life expectancy was 34.7 years for men and 30.7 years for women compared with 14.9 to 15.3 years for men and 10.6 years for women at age 55. This gender disparity attenuated for people with a spouse or other intimate partner. At age 55, men in very good or excellent health on average gained 5-7 years of sexually active life compared with their peers in poor or fair health. Women in very good or excellent health gained 3-6 years compared with women in poor or fair health. Conclusion Sexual activity, good quality sexual life, and interest in sex were higher for men than for women and this gender gap widened with age. Sexual activity, quality of sexual life, and interest in sex were positively associated with health in middle age and later life. Sexually active life expectancy was longer for men, but men lost more years of sexually active life as a result of poor health than women.

Leander Girrbach, Stephan Alaniz, Genevieve Smith et al.

With the increasing use of image generation technology, understanding its social biases, including gender bias, is essential. This paper presents a large-scale study on gender bias in text-to-image (T2I) models, focusing on everyday situations. While previous research has examined biases in occupations, we extend this analysis to gender associations in daily activities, objects, and contexts. We create a dataset of 3,217 gender-neutral prompts and generate 200 images over 5 prompt variations per prompt from five leading T2I models. We automatically detect the perceived gender of people in the generated images and filter out images with no person or multiple people of different genders, leaving 2,293,295 images. To enable a broad analysis of gender bias in T2I models, we group prompts into semantically similar concepts and calculate the proportion of male- and female-gendered images for each prompt. Our analysis shows that T2I models reinforce traditional gender roles and reflect common gender stereotypes in household roles. Women are predominantly portrayed in care and human-centered scenarios, and men in technical or physical labor scenarios.

Jiayi Li, Xiao Liu, Yansong Feng

Large Language Model (LLM)-based multi-agent systems are increasingly used to simulate human interactions and solve collaborative tasks. A common practice is to assign agents with personas to encourage behavioral diversity. However, this raises a critical yet underexplored question: do personas introduce biases into multi-agent interactions? This paper presents a systematic investigation into persona-induced biases in multi-agent interactions, with a focus on social traits like trustworthiness (how an agent's opinion is received by others) and insistence (how strongly an agent advocates for its opinion). Through a series of controlled experiments in collaborative problem-solving and persuasion tasks, we reveal that (1) LLM-based agents exhibit biases in both trustworthiness and insistence, with personas from historically advantaged groups (e.g., men and White individuals) perceived as less trustworthy and demonstrating less insistence; and (2) agents exhibit significant in-group favoritism, showing a higher tendency to conform to others who share the same persona. These biases persist across various LLMs, group sizes, and numbers of interaction rounds, highlighting an urgent need for awareness and mitigation to ensure the fairness and reliability of multi-agent systems.

Leander Girrbach, Stephan Alaniz, Genevieve Smith et al.

Vision-language models trained on large-scale multimodal datasets show strong demographic biases, but the role of training data in producing these biases remains unclear. A major barrier has been the lack of demographic annotations in web-scale datasets such as LAION-400M. We address this gap by creating person-centric annotations for the full dataset, including over 276 million bounding boxes, perceived gender and race/ethnicity labels, and automatically generated captions. These annotations are produced through validated automatic labeling pipelines combining object detection, multimodal captioning, and finetuned classifiers. Using them, we uncover demographic imbalances and harmful associations, such as the disproportionate linking of men and individuals perceived as Black or Middle Eastern with crime-related and negative content. We also show that a linear fit predicts 60-70% of gender bias in CLIP and Stable Diffusion from direct co-occurrences in the data. Our resources establish the first large-scale empirical link between dataset composition and downstream model bias. Code is available at https://github.com/ExplainableML/LAION-400M-Person-Centric-Annotations.