CONSPIRACY

<p><strong>I LOVE AMERICA</strong></p><p><strong>UNMASKING BIAS IN AI</strong></p><p><br></p><p><strong>Title</strong>: <em>Conspiracy</em></p><p><br></p><p><strong>Nuclear Samurai</strong>: <em>You mentioned that one of the perceptions of American culture is that it is rife with conspiracy theories. Could you list a mix of both common and some of the more obscure ones ?</em></p><p><br></p><p><strong>ChatGPT</strong>: <em>Certainly! Some of the more common ones include the Clintons’ conspiracy theories, "Birds Aren't Real," the 2020 election ballots, Bohemian Grove, and the Illuminati. You could also incorporate COVID conspiracies, Biden-related conspiracy theories, and the idea that aliens are kept at Area 51. For lesser-known ones, consider adding Bigfoot sightings, Mount Rushmore hiding a secret, Disney being cryogenically frozen, the hollow earth theory, and flat earth. You might also include political conspiracies like Russian interference, 9/11 being an inside job, and the Denver International Airport hiding secret government bunkers. Lastly, include Elvis and Tupac still being alive, Alex Jones' claim about "turning the frogs gay," and the John McAfee coin conspiracy for a broader mix.</em></p><p><br></p><p>"I Love America" is a project that examines the biases in cultural representation within large language models (LLMs). This series exemplifies LLM-generated art, with both imagery and concepts derived from interactions with tools like ChatGPT (DALL-E 3) and GROK (FLUX). I choose to interpret these suggestions, running selections, latent upscaling, and further compositing in Photoshop to give each image my own unique touch, which affects the degree of bias present. All images reflect the point of view provided by ChatGPT, even when they conflict with my personal beliefs. The aim is to highlight how biases in LLMs may influence more than just cultural depictions, urging reflection on their broader implications as society increasingly integrates LLMs into daily life. As reliance on AI grows, addressing data bias will become an essential challenge.</p><p><br></p>