Related papers: Visual Stereotypes of Autism Spectrum in DALL-E, Stable Diffusion, SDXL, and Midjourney

Visual Stereotypes of Autism Spectrum in DALL-E, Stable Diffusion, SDXL, and Midjourney

URL: http://arxiv.org/abs/2407.16292v2
Date: Wed, 24 Jul 2024 07:15:26 GMT
Title: Visual Stereotypes of Autism Spectrum in DALL-E, Stable Diffusion, SDXL, and Midjourney
Authors: Maciej Wodziński, Marcin Rządeczka, Anastazja Szuła, Marta Sokół, Marcin Moskalewicz,
Abstract summary: Our study investigated how text-to-image models unintentionally perpetuate non-rational beliefs regarding autism. The research protocol involved generating images based on 53 prompts aimed at visualizing concrete objects and abstract concepts related to autism.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Avoiding systemic discrimination requires investigating AI models' potential to propagate stereotypes resulting from the inherent biases of training datasets. Our study investigated how text-to-image models unintentionally perpetuate non-rational beliefs regarding autism. The research protocol involved generating images based on 53 prompts aimed at visualizing concrete objects and abstract concepts related to autism across four models: DALL-E, Stable Diffusion, SDXL, and Midjourney (N=249). Expert assessment of results was performed via a framework of 10 deductive codes representing common stereotypes contested by the community regarding their presence and spatial intensity, quantified on ordinal scales and subject to statistical analysis of inter-rater reliability and size effects. The models frequently utilised controversial themes and symbols which were unevenly distributed, however, with striking homogeneity in terms of skin colour, gender, and age, with autistic individuals portrayed as engaged in solitary activities, interacting with objects rather than people, and displaying stereotypical emotional expressions such as pale, anger, or sad. Secondly we observed representational insensitivity regarding autism images despite directional prompting aimed at falsifying the above results. Additionally, DALL-E explicitly denied perpetuating stereotypes. We interpret this as ANNs mirroring the human cognitive architecture regarding the discrepancy between background and reflective knowledge, as justified by our previous research on autism-related stereotypes in humans.

Related papers

Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on Fairness-Related Queries [85.909363478929]
In this study, we focus on 19 real-world statistics collected from authoritative sources. We develop a checklist comprising objective and subjective queries to analyze behavior of large language models. We propose metrics to assess factuality and fairness, and formally prove the inherent trade-off between these two aspects.
arXiv Detail & Related papers (2025-02-09T10:54:11Z)
Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing Atypical Facial Expressions in Children with Autism Spectrum Disorder [2.3001245059699014]
We introduce a novel dataset, Hugging Rain Man, which includes facial action units (AUs) manually annotated by FACS experts for both children with ASD and typical development (TD) The dataset comprises a rich collection of posed and spontaneous facial expressions, totaling approximately 130,000 frames, along with 22 AUs, 10 Action Descriptors (ADs) and atypicality ratings.
arXiv Detail & Related papers (2024-11-21T02:51:52Z)
Hear Me, See Me, Understand Me: Audio-Visual Autism Behavior Recognition [47.550391816383794]
We introduce a novel problem of audio-visual autism behavior recognition. Social behavior recognition is an essential aspect previously omitted in AI-assisted autism screening research. We will release our dataset, code, and pre-trained models.
arXiv Detail & Related papers (2024-03-22T22:52:35Z)
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
arXiv Detail & Related papers (2023-11-16T07:22:56Z)
Stable Bias: Analyzing Societal Representations in Diffusion Models [72.27121528451528]
We propose a new method for exploring the social biases in Text-to-Image (TTI) systems. Our approach relies on characterizing the variation in generated images triggered by enumerating gender and ethnicity markers in the prompts. We leverage this method to analyze images generated by 3 popular TTI systems and find that while all of their outputs show correlations with US labor demographics, they also consistently under-represent marginalized identities to different extents.
arXiv Detail & Related papers (2023-03-20T19:32:49Z)
Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles [52.77024349608834]
We analyze the influence of replacing a DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.
arXiv Detail & Related papers (2022-11-12T23:28:54Z)
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale [61.555788332182395]
We investigate the potential for machine learning models to amplify dangerous and complex stereotypes. We find a broad range of ordinary prompts produce stereotypes, including prompts simply mentioning traits, descriptors, occupations, or objects.
arXiv Detail & Related papers (2022-11-07T18:31:07Z)
Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes for Detection of Autism Spectrum Disorder [5.908259551646475]
Graph-based learning techniques have demonstrated impressive results on resting-state functional magnetic resonance imaging (rs-fMRI) data. IMAGIN achieves a 5-fold cross-validation accuracy of 79.25%, which surpasses the current state-of-the-art by 1.5%.
arXiv Detail & Related papers (2022-10-30T01:43:17Z)
A Two-stage Multi-modal Affect Analysis Framework for Children with Autism Spectrum Disorder [3.029434408969759]
We present an open-source two-stage multi-modal approach leveraging acoustic and visual cues to predict three main affect states of children with ASD's affect states in real-world play therapy scenarios. This work presents a novel way to combine human expertise and machine intelligence for ASD affect recognition by proposing a two-stage schema.
arXiv Detail & Related papers (2021-06-17T01:28:53Z)
Gaze-based Autism Detection for Adolescents and Young Adults using Prosaic Videos [35.54632105027475]
We demonstrate that by monitoring a user's gaze as they watch commonplace (i.e., not specialized, structured or coded) video, we can identify individuals with autism spectrum disorder. We recruited 35 autistic and 25 non-autistic individuals, and captured their gaze using an off-the-shelf eye tracker connected to a laptop. Within 15 seconds, our approach was 92.5% accurate at identifying individuals with an autism diagnosis.
arXiv Detail & Related papers (2020-05-26T18:14:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.