Cognitive reappraisal is a key strategy in emotion regulation, involving reinterpretation of emotionally charged stimuli to alter affective responses. Despite its central role in clinical and cognitive science, real-world reappraisal interventions remain cognitively demanding, abstract, and primarily verbal. This reliance on higher-order cognitive and linguistic processes is often impaired in individuals with trauma or depression, limiting the effectiveness of standard approaches. Here, we propose a novel, visually based augmentation of cognitive reappraisal by integrating large-scale text-to-image diffusion models into the emotional regulation process. Specifically, we introduce a system in which users reinterpret emotionally negative images via spoken reappraisals, which are transformed into supportive, emotionally congruent visualizations using stable diffusion models with a fine-tuned IP-adapter. This generative transformation visually instantiates users' reappraisals while maintaining structural similarity to the original stimuli, externalizing and reinforcing regulatory intent. To test this approach, we conducted a within-subject experiment (N = 20) using a modified cognitive emotion regulation (CER) task. Participants reappraised or described aversive images from the International Affective Picture System (IAPS), with or without AI-generated visual feedback. Results show that AI-assisted reappraisal significantly reduced negative affect compared to both non-AI and control conditions. Further analyses reveal that sentiment alignment between participant reappraisals and generated images correlates with affective relief, suggesting that multimodal coherence enhances regulatory efficacy. These findings demonstrate that generative visual input can support cogitive reappraisal and open new directions at the intersection of generative AI, affective computing, and therapeutic technology.

Recent advances in generative AI have enabled visual content creation through text-to-image (T2I) generation. However, despite their creative potential, T2I models often replicate and amplify societal stereotypes -- particularly those related to gender, race, and culture -- raising important ethical concerns. This paper proposes a theory-driven bias detection rubric and a Social Stereotype Index (SSI) to systematically evaluate social biases in T2I outputs. We audited three major T2I model outputs -- DALL-E-3, Midjourney-6.1, and Stability AI Core -- using 100 queries across three categories -- geocultural, occupational, and adjectival. Our analysis reveals that initial outputs are prone to include stereotypical visual cues, including gendered professions, cultural markers, and western beauty norms. To address this, we adopted our rubric to conduct targeted prompt refinement using LLMs, which significantly reduced bias -- SSI dropped by 61% for geocultural, 69% for occupational, and 51% for adjectival queries. We complemented our quantitative analysis through a user study examining perceptions, awareness, and preferences around AI-generated biased imagery. Our findings reveal a key tension -- although prompt refinement can mitigate stereotypes, it can limit contextual alignment. Interestingly, users often perceived stereotypical images to be more aligned with their expectations. We discuss the need to balance ethical debiasing with contextual relevance and call for T2I systems that support global diversity and inclusivity while not compromising the reflection of real-world social complexity.

The rapid advancements in AI technologies have revolutionized the production of graphical content across various sectors, including entertainment, advertising, and e-commerce. These developments have spurred the need for robust evaluation methods to assess the quality and realism of AI-generated images. To address this, we conducted three studies. First, we introduced and validated a questionnaire called Visual Verity, which measures photorealism, image quality, and text-image alignment. Second, we applied this questionnaire to assess images from AI models (DALL-E2, DALL-E3, GLIDE, Stable Diffusion) and camera-generated images, revealing that camera-generated images excelled in photorealism and text-image alignment, while AI models led in image quality. We also analyzed statistical properties, finding that camera-generated images scored lower in hue, saturation, and brightness. Third, we evaluated computational metrics' alignment with human judgments, identifying MS-SSIM and CLIP as the most consistent with human assessments. Additionally, we proposed the Neural Feature Similarity Score (NFSS) for assessing image quality. Our findings highlight the need for refining computational metrics to better capture human visual perception, thereby enhancing AI-generated content evaluation.

While the term `art' defies any concrete definition, this paper aims to examine how digital images produced by generative AI systems, such as Midjourney, have come to be so regularly referred to as such. The discourse around the classification of AI-generated imagery as art is currently somewhat homogeneous, lacking the more nuanced aspects that would apply to more traditional modes of artistic media production. This paper aims to bring important philosophical considerations to the surface of the discussion around AI-generated imagery in the context of art. We employ existing philosophical frameworks and theories of language to suggest that some AI-generated imagery, by virtue of its visual properties within these frameworks, can be presented as `readymades' for consideration as art.

ArtStation is probably the most important website on the whole internet for professional artists, especially those working in entertainment fields like video games (most of our Fine Art links, for example, point there). Which is why the site's continued allowance of AI-generated imagery has become a point of contention with its users. The technology, which is rotten to its core, is of particular concern to a community who make a living creating art, and as such should also be a concern to the companies responsible for owning and hosting that community. But as of today, ArtStation has no policy directly restricting the hosting or display of AI-generated imagery on the site, which has led to repeated instances where images made by computers, and not humans, have floated to the top of ArtStation's "Explore" section, its most popular means of showcasing the work of artists. That is, understandably, pissing a lot of people off.

On Monday, Adobe announced that its stock photography service, Adobe Stock, would begin allowing artists to submit AI-generated imagery for sale, Axios reports. The move comes during Adobe's embrace of image synthesis and also during industry-wide efforts to deal with the rapidly growing field of AI artwork in the stock art business, including earlier announcements from Shutterstock and Getty Images. Submitting AI-generated imagery to Adobe Stock comes with a few restrictions. The artist must own (or have the rights to use) the image, AI-synthesized artwork must be submitted as an illustration (even if photorealistic), and it must be labeled with "Generative AI" in the title. Further, each AI artwork must adhere to Adobe's new Generative AI Content Guidelines, which require the artist to include a model release for any real person depicted realistically in the artwork.

AI-generated art is a new frontier rife with potential. For example, look no further than this AI-powered experiment that creates kaleidoscopic visual landscapes for composed music. A collaboration between quirky synth and hardware brand Teenage Engineering and design studios Modem and Bureau Cool, the project draws inspiration from the neurological condition synesthesia. This rare phenomenon leads the brain to perceive sensory input for several senses instead of one. For example, a listener with synesthesia may see music instead of only hearing it, observing color, movement and shape in response to musical patterns.

Microsoft doesn't say whether its Designer app can generate images of people, for example. The company says OpenAI has filtered "explicit sexual and violent content from the dataset used to train the model" and that it's also "deployed filters to limit generation of images that violate content policy" and "additional query blocking on sensitive topics." But, such filters are always permeable, and the tools could still be used to generate troubling imagery -- from NSFW creations to offensive or insensitive content.