Add to ORKGThe paper discusses the potential of large vision-language models as objects of interest for empirical cultural studies. Focusing on the comparative analysis of outputs from two popular text-to-image synthesis models, DALL-E 2 and Stable Diffusion, the paper tries to tackle the pros and cons of striving towards culturally agnostic vs. culturally specific AI models. The paper discusses several examples of memorization and bias in generated outputs which showcase the trade-off between risk mitigation and cultural specificity, as well as the overall impossibility of developing culturally agnostic models.Comment: Accepted for "Cultures in AI/AI in Culture" NeurIPS 2022 Worksho
Machine learning models are now able to convert user-written text descriptions into naturalistic ima...
Generative AIs produce creative outputs in the style of human expression. We argue that encounters w...
Latent diffusion models excel at producing high-quality images from text. Yet, concerns appear about...
One challenge in text-to-image (T2I) generation is the inadvertent reflection of culture gaps presen...
Purpose – The paper aims to expand on the works well documented by Joy Boulamwini and Ruha Benjamin ...
In a recent letter, Dillion et. al (2023) make various suggestions regarding the idea of artificiall...
As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingly prevalent and seei...
As neural language models begin to change aspects of everyday life, they understandably attract crit...
Text-conditioned image generation models have recently shown immense qualitative success using denoi...
Recently, DALL-E, a multimodal transformer language model, and its variants, including diffusion mod...
In this paper I argue that, given the way that AI models work and the way that ordinary human ration...
We survey a current, heated debate in the AI research community on whether large pre-trained languag...
Assessments of algorithmic bias in large language models (LLMs) are generally catered to uncovering ...
International audienceIt is well known that AI-based language technology—large language models, mach...
Deep generative models produce data according to a learned representation, e.g. diffusion models, th...
Machine learning models are now able to convert user-written text descriptions into naturalistic ima...
Generative AIs produce creative outputs in the style of human expression. We argue that encounters w...
Latent diffusion models excel at producing high-quality images from text. Yet, concerns appear about...
One challenge in text-to-image (T2I) generation is the inadvertent reflection of culture gaps presen...
Purpose – The paper aims to expand on the works well documented by Joy Boulamwini and Ruha Benjamin ...
In a recent letter, Dillion et. al (2023) make various suggestions regarding the idea of artificiall...
As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingly prevalent and seei...
As neural language models begin to change aspects of everyday life, they understandably attract crit...
Text-conditioned image generation models have recently shown immense qualitative success using denoi...
Recently, DALL-E, a multimodal transformer language model, and its variants, including diffusion mod...
In this paper I argue that, given the way that AI models work and the way that ordinary human ration...
We survey a current, heated debate in the AI research community on whether large pre-trained languag...
Assessments of algorithmic bias in large language models (LLMs) are generally catered to uncovering ...
International audienceIt is well known that AI-based language technology—large language models, mach...
Deep generative models produce data according to a learned representation, e.g. diffusion models, th...
Machine learning models are now able to convert user-written text descriptions into naturalistic ima...
Generative AIs produce creative outputs in the style of human expression. We argue that encounters w...
Latent diffusion models excel at producing high-quality images from text. Yet, concerns appear about...