This essay delves into the world of AI-generated images and the tools that create them. These type of tools became widely available in 2022, allowing users to generate images based on instructions written in plain text. The objective of this essay is to explore the landscape of AI-generated images based on text instructions, specifically focusing on the perspectives of key stakeholders: the artist community, the AI companies who create the tools, and the individuals utilising them. The essay draws upon Walter Benjamin's thoughts on politics, mass production, and authenticity as articulated in the essay "The Work of Art in the Age of Mechanical Reproduction," published in 1935. The material has been analysed through a phenomenological lens, considering how AI-generated images are perceived and their relationship to representation. The text reflects on who benefits from AI tools and whether it happens at the expense of others. Consequently, the essay also examines power structures and the power distribution among users of AI tools, the AI companies developing these tools, and existing artists not utilising AI tools. The subject is complex, and the essay identifies concerns within the artist community regarding potentially being replaced by AI tools constructed using the work of human artists without their knowledge or consent. Simultaneously, the essay recognises that AI tools can offer individuals who would not typically engage in image creation a pathway to creative expression.

Our research study focuses on exploring how 3D-students view AI image generation tools usefullness as concept images in 3D production. We conducted interviews with five 3D students to gain insight into how useful they find AI-generated images in 3D production. By collecting information through interview method, participants' responses were analyzed using thematic analysis. Their responses were combined with existing theories in concept art, disruptive technologies, and sparing technologies. Our main findings show that students found images made by using AI can be used as inspiration images and moodboards but have difficulty being used for anything else in a 3D production, as AI was poor at reproducing the same object multiple times. The same object couldn't be obtained from multiple angles which limits usability. However the 3D-students think there is potential in using the images as a foundation that is then further developed by a human. / Vår forskningsstudie fokuserar på att utforska hur 3D-studenter ser på användbarheten av AI-bildgenereringsverktyg som konceptbilder i 3D-produktion. Vi genomförde intervjuer med fem 3D-studenter för att få insikt i hur användbara de tycker att AI-genererade bilder är i 3D-produktion. Genom att samla in information genom intervjuer analyserades deltagarnas svar med tematisk analys. Deras svar kombinerades med befintliga teorier inom konstkoncept, störande teknologier och sparsamma teknologier. Våra huvudsakliga resultat visar att studenterna fann att bilder skapade med AI kan användas som inspirationsbilder och moodboards men har svårigheter att användas för något annat i 3D-produktion, eftersom AI hade svårt att reproducera samma objekt flera gånger. Samma objekt kunde inte erhållas från olika vinklar vilket begränsar användbarheten. Dock tror 3D-studenterna att det finns potential att använda bilderna som en grund som sedan utvecklas ytterligare av en människa.

Facial recognition technology has become a ubiquitous tool in security and personal identification. However, the rise of this technology has been accompanied by concerns over inherent biases, particularly regarding ethnic and gender. This thesis examines the extent of these biases by focusing on the influence of dataset imbalances in facial recognition algorithms. We employ a structured methodological approach that integrates AI-generated images to enhance dataset diversity, with the intent to balance representation across ethnics and genders. Using the ResNet and Vgg model, we conducted a series of controlled experiments that compare the performance impacts of balanced versus imbalanced datasets. Our analysis includes the use of confusion matrices and accuracy, precision, recall and F1-score metrics to critically assess the model’s performance. The results demonstrate how tailored augmentation of training datasets can mitigate bias, leading to more equitable outcomes in facial recognition technology. We present our findings with the aim of contributing to the ongoing dialogue regarding AI fairness and propose a framework for future research in the field.