Overview and session details of the ESB2025 congress.
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Towards Fairness in AI: The Critical Role of Representative Training Data - Catherine Jutzeler, ETH Zurich
BIASMECHANICS: Does an unconscious bias still persist in biomechanics, positioning males as the default in human research? A meta-analysis on the Journal of Biomechanics 2024 publications - Frank Gijsen,
Department of Biomechanical Engineering Faculty of Mechanical Engineering, Delft University of Technology & Department of Biomedical Engineering Faculty of Cardiology, Erasmus University Medical Center
Session Abstract
BIASMECHANICS: Does an unconscious bias still persist in biomechanics, positioning males as the default in human research? A meta-analysis on the Journal of Biomechanics 2024 publications Eline van der Kruk, Frank Gijsen
Articles published in the Journal of Biomechanics still reflect bias, with males positioned as the default in human research. This meta-analysis on the 2024 articles reveals a large disparity in female representation. One in four studies showed an imbalance (<30 % female representation) favouring male participants, while only 8 % favoured females. Male-only studies outnumbered female-only studies by over fivefold. Of particular concern is that male-only studies often lack justification for their single-gender focus, whereas female-only studies typically provide clear reasoning. This inconsistency not only lacks accountability but also reinforces the notion that male data is the standard in biomechanics research. I named this issue biasmechanics to encourage efforts to address them. While there are valid scientific reasons for focusing on specific gender/sex groups, this should not be the default. Authors must consider sex- and gender-based differences, and reviewers and editors should adopt stricter standards for accepting articles with unjustified imbalances. The Journal of Biomechanics could establish standardized guidelines promoting equitable representation in research. Exclusions of any sex or gender must include clear scientific justification in the introduction and methodology sections. The discussion and limitations sections should assess the implications of such exclusions, including their effects on validity, generalizability, and bias. If appropriate, titles and abstracts should clearly indicate single-sex or gender-specific studies to ensure transparency about the research’s scope and applicability. By collectively affirming as a scientific community that, except for legitimate scientific justification, we oppose the exclusion of female participants, we can shift the default approach in our research studies.
Presentations
ID: 593
INVESTIGATING BONE DENSITY AND STRENGTH ACROSS SEX AND AGE USING MACHINE LEARNING AND FINITE ELEMENT MODELING
B. E Matheson1, M. Walle1, C. Ye1,2, S. K Boyd1
1University of Calgary, Calgary, Alberta, Canada; 2Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
