Bias in AI: A Comprehensive Examination of Factors and Improvement Strategies

Abstract

- Artificial intelligence is becoming extremely popular in our lives, being used in every sector, from job applications to medical diagnoses. AI is often biased due to various factors, ranging from biased training data to a lack of diversity and the designing and modeling team. Bias in AI is this research paper’s focus, which starts by discussing AI development and a basic understanding of how AI models work. Later, bias in AI and its reasons are discussed with examples, along with a comparison of bias in different AI models. Image generation AI models such as Stable Diffusion and DALL-E 2, along with text generation AIs such as ChatGPT, are analyzed. Bias in AI in different respects, such as Gender, Religion, and Race, has been explored in detail. Towards the end, steps that have been taken to mitigate bias have been discussed.