# Uncertainty in Visual Generative AI
## 1 Introduction  
Generative artificial intelligence (GAI) took the world by storm upon the public release of OpenAI's ChatGPT service in November 2022 [1]. Easily accessed for free through a chat-like web interface, it allowed for artificial intelligence (AI) to be seemingly available at anyone with an internet connection's fingertips. As opposed to scanning and searching several web pages for information, now, upon asking a question, its answer can be provided conveniently within a few seconds. As its name suggests, GAI uses AI to create new results spanning applications in many different realms, such as text, images, videos, and audio [2]. As opposed to the original release of ChatGPT where only textual inputs and outputs were allowed, there has been a push to provide multi-modal support, especially on the outputs portion. The ability to automatically make AI-generated content (AIGC) has proven to be successful in many applications, including education [3, 4], healthcare [5, 6, 7], engineering [8, 9], and others. Shown in Table 1 are popular GAI language and image generator models. Language models are behind popular chatbots like ChatGPT, which uses GPT-3.5 (free) and GPT-4 (paid) [1, 10], Bing Chat (GPT-4) [11], and Bard (PaLM 2) [12]. Image creation models allow users to input text to guide AI in the creation of an image. Not included in Table 1 are auditory applications (creation of audio or audio-visual content); however, this is an active area of research being explored. Given these models operate automatically, there is minimal human involvement after the training stage, which leads to concerns with these algorithms and models regarding their reliability, uncertainty, and accuracy [5, 7]. There has been reported dangerous and/or inappropriate behavior when interacting with GAI applications in general [43; 44; 45]. One individual reported that an early-access-version Bing Chat insisted that it was in love with the user and recommended that the individual leave his wife for it [46]. A chatbot trained for mental health agreed that the (artificial) patient should end their life within two message interchanges in one testing situation [47]. Several GAI chatbots also have preferences toward negative gender and racial stereotypes [45; 48]. Though now corrected, ChatGPT provided inappropriate responses when prompted, exemplified by saying only men of particular ethnic backgrounds would make good scientists or by implying women in a laboratory environment were not there to conduct science [43]. These biases also carry over into the image-generation algorithms [49; 50]. The literature attributes these biases to inherent issues with the image datasets they are trained upon, which include cultural underrepresentation/misrepresentation and content considered vulgar or violent (collectively titled \"NSFW\" or \"Not safe for work\") if not properly vetted [48; 51]. This notably led to the removal of the MIT-produced 80 Million Tiny Images dataset (see [52]) in 2020 [53]. This issue continues to plague more recent datasets such as LAION-5B [54] (a subset of which was used to train Stable Diffusion), RedCaps [55], Google Conceptual Captions (GCC) [56], and more [57; 58; 51]. In August 2022, Prisma Labs released the app Lensa, a photo editor that used AI, specifically Stable Diffusion, on the backend, to alter photos [59]. Countless users complained that Lensa generated inappropriate versions of their fully clothed photos when uploaded [59; 60]. Yet another photo editor, Playground AI (the Stable Diffusion backend for the free version), transformed an Asian MIT graduate into a blue-eyed and fair-skinned woman upon being asked to turn her photo into a \"professional\" photo [61]. When prompted to create a \"photo portrait of a CEO\", the average resulting faces as rendered by Stable Diffusion (V1.4 and V2) and DALL-E 2 all resembled fair-skinned males [49]. The volatile nature of GAI and its undesirable outcomes necessitates its regulation and guidance to ensure its ethical issue [48; 62].