Jurnal STAI Al-Hamidiyah
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Humanlike AI Can Strengthen Women’s Belief in Sexist Stereotypes
No full textCan interactions with humanlike AI strengthen harmful stereotypical beliefs in people from predisposed and vulnerable groups? Anthropomorphic features have been shown to increase individuals’ perceptions of AI’s trustworthiness, but AI also is known to repeat gender stereotypes, raising the concern that anthropomorphic AI chatbots can strengthen stereotypes in individuals who are predisposed to these beliefs. Consistent with this prediction, we report results from four preregistered experiments on U.S. adults (N = 2,774) showing that politically conservative women believed the archetypal gender-math stereotype in a chatbot's response to be more accurate when the chatbot had lifelike features. The effect was mediated by perceived anthropomorphism (specifically, mind perception) and trustworthiness, and we ruled out an alternative cognitive mechanism for this effect. Neither liberal women nor conservative men showed this effect for the gender-math stereotype; however, our final experiment shows that liberal women showed the same indirect influence for a different gender stereotype that they are more predisposed to believe. In a formal model, we speculate that ideological predisposition to specific gender stereotypes and social identity threat may converge, making women more susceptible to believing sexist stereotypes asserted by anthropomorphic AI chatbots. We argue that this effect could be prevented by socially-conscious AI developers who de-anthropomorphize AI applications. Future research should test whether this effect occurs for other identity groups with stereotypes they are predisposed to believe
Urbanization and Health in the Context of Sustainable Development
No full textThe primary objective of the interdisciplinary project is to develop an innovative theoretical research model that explores the connection between urbanization and mental and physical health. This will involve examining the role of loneliness within the context of sustainable development. The research will be carried out through a prospective study, and the findings will be shared through scientific articles and recommendations for local and regional communities.
The project "Urbanization and Health in the Context of Sustainable Development - an international prospective study" (no. NdS-II/SN/0391/2024/012) is financed by state budget funds granted by the Minister of Education and Science within the framework of the Science for Society II Program in Poland to Academy of Silesia in Katowice, Poland. The project leader is Dominika Ochnik
DECIDE - Decision-Enabling Confirmation of Innovative Discoveries and exploratory Evidence
No full textThe DECIDE project works towards improving translation from preclinical findings by supporting multi-laboratory confirmatory research projects across Germany. Methodological support is provided through individual counseling sessions, educational workshops and online seminars. The project further aims to perform a meta-analysis of the confirmatory studies to develop a best practice framework for conducting preclinical trials.
See also: https://www.bihealth.org/en/translation/innovation-enabler/quest-center/projects/project/decide-phase-i
Google search data for psychological scientists: A tutorial and best practices
No full textGoogle search data has been described as the most important dataset on human nature ever assembled, giving nearly instant access to datasets that can provide insights to questions about various topics, including disease, racism, religiosity, well-being, and mental health. These data are customizable—researchers can compare search volume across most of the world or zoom into specific geographic regions; access hourly data within the last week or look at monthly data since 2004. However, they have important limitations. We provide a comprehensive overview and tutorial, covering (a) how Google Trends data are calculated, how reliable they are, and why some results yield low-quality data, (b) how to create custom datasets beyond what Google Trends provides by default (creating long trends of daily data, creating datasets with many cross-sectional comparisons, creating panel data), (c) a list of potential promises and pitfalls of Google Trends data, and (d) recommendations to ensure data quality and sound interpretation
The Social Structure of Scientific Evaluation: AI, Benchmarking, and the Deep Learning Monoculture
No full textEvaluation systems are central organizing institutions in science that coordinate consensus and drive epistemic trajectories. Scientific fields have traditionally relied on "organic" evaluation systems (e.g., peer review, citation) where consensus emerges gradually across multiple epistemic values. This paper highlights artificial intelligence research (AIR) as a potent counterpoint to this model. Drawing on interviews with key actors, computational analyses, and archival materials spanning AIR’s history (1956–2021), we examine how AI evolved from a discipline with weak organic evaluation into a field driven by benchmarking, a “formal” evaluation system that defines progress quantitatively as state-of-the-art accuracy on commercial tasks. We demonstrate that benchmarking came to dominate through an intricate symbiosis with deep learning: benchmarking rewards accuracy, which large-scale deep learning uniquely excelled at, while deep learning’s opacity made organic evaluation increasingly difficult. This symbiosis restructured the field organizationally, epistemically, and materially into a “monoculture” dedicated to scaling. While enabling breakneck progress, monoculture discouraged exploration of alternatives with different epistemic strengths. As AI spreads to other knowledge fields (from science to law to art) benchmarking will accompany it. Our findings thus highlight the risk that formalization of evaluation can lead to monoculture in other creative domains
Validation of the French Adaptation of the Fear of Missing Out (FoMO) Scale
No full textAlthough the Fear of Missing Out (FoMO) scale has been widely used, its French adaptation has not been validated and its psychometric properties have not been tested. Addressing this literature gap, we propose a French validation of the measure, investigating its factorial structure and testing its invariance regarding age, sex, education, and timepoints. Using an online longitudinal study (2-waves) with a lifespan sample (N = 543, age range18 to 98 years, M = 46.8), the results showed that the French version had a different factorial structure than the original scale, identifying three subfactors that loaded to one superfactor, using 8 out of 10 original items. Measurement invariance testing revealed that the scale can be used for mean level comparisons between sexes, educational levels, and timepoints, while caution is suggested regarding age-groups. Thus, the French version of the FoMO scale is a reliable measure that can be used in French-speaking populations with various characteristics
Replication Package: Overwork and the Use of Paid Leave and Flexible Work Policies in U.S. Workplaces
No full textThis study uses the 2011 American Time Use Survey Leave Module (supplemented with the 2017-2018 versions), O*NET and the ACSs to examine the associations between the prevalence of long work hours in the occupation and using paid leave and flexible work policies
