Marcin Rządeczka

Marcin Rządeczka, Maciej Wodziński, Kacper Zacharski et al.

We present experimental findings from a study (N=99) examining how intellectual humility (IH), i.e., the metacognitive awareness of epistemic limitations, affects the evaluation of AI-generated health dialogues varying in scientific rigor. Participants were randomly assigned to evaluate one of three dialogues about exercise and mental health: scientifically accurate, moderately pseudoscientific, or strongly pseudoscientific. Results reveal that IH functions as a selective cognitive filter. Individuals with higher humility scores rated pseudoscientific content as significantly less credible, while showing no correlation with credibility assessments of accurate content. Crucially, humility did not predict the ability to identify AI as the source of dialogues, suggesting that epistemic vigilance operates on content quality rather than source attribution. We interpret these findings through an evolutionary lens, proposing that IH represents an ancestral adaptation for navigating informationally uncertain environments. It remains effective at detecting exploitation attempts in AI-generated content, despite humans lacking evolved mechanisms for detecting AI sources. The study contributes to understanding how foundation models might improve or undermine human epistemic defenses, especially in health communication contexts.

Maciej Wodziński, Marcin Rządeczka, Anastazja Szuła et al.

Avoiding systemic discrimination of neurodiverse individuals is an ongoing challenge in training AI models, which often propagate negative stereotypes. This study examined whether six text-to-image models (Janus-Pro-7B VL2 vs. VL3, DALL-E 3 v. April 2024 vs. August 2025, Stable Diffusion v. 1.6 vs. 3.5, SDXL v. April 2024 vs. FLUX.1 Pro, and Midjourney v. 5.1 vs. 7) perpetuate non-rational beliefs regarding autism by comparing images generated in 2024-2025 with controls. 53 prompts aimed at neutrally visualizing concrete objects and abstract concepts related to autism were used against 53 controls (baseline total N=302, follow-up experimental 280 images plus 265 controls). Expert assessment measuring the presence of common autism-related stereotypes employed a framework of 10 deductive codes followed by statistical analysis. Autistic individuals were depicted with striking homogeneity in skin color (white), gender (male), and age (young), often engaged in solitary activities, interacting with objects rather than people, and exhibiting stereotypical emotional expressions such as sadness, anger, or emotional flatness. In contrast, the images of neurotypical individuals were more diverse and lacked such traits. We found significant differences between the models; however, with a moderate effect size, and no differences between baseline and follow-up summary values, with the ratio of stereotypical themes to the number of images similar across all models. The control prompts showed a significantly lower degree of stereotyping with large size effects, confirming the hidden biases of the models. In summary, despite improvements in the technical aspects of image generation, the level of reproduction of potentially harmful autism-related stereotypes remained largely unaffected.