Bias and Fairness in Generative Models: Ethical Challenges and Engineering Solutions

Generative Artificial Intelligence (GAI) has emerged as a transformative technology, demonstrating a remarkable capacity to autonomously produce human-like content across diverse domains such as text, images, code, and media. 

GAI offers substantial benefits and influences critical decisions, particularly in fields such as education, healthcare, and the creative industries. However, it also introduces complex ethical challenges that necessitate careful consideration. 

A prominent concern within GAI is the pervasive issue of bias and fairness. Generative models, trained on vast datasets, can inadvertently learn and amplify societal stereotypes, prejudices, and historical inequalities, leading to discriminatory or unfair outcomes across various applications. While bias and fairness are central, GAI’s generative capabilities also amplify other significant ethical dilemmas, introducing new risks not typically posed by traditional AI systems. These include misinformation and deepfakes, data privacy violations, intellectual property issues, and challenges related to accountability and explainability. Understanding these interconnected challenges is crucial for a holistic approach to responsible GAI development. 

This systematic literature review focuses specifically on bias and fairness in GAI systems, recognizing them as key concerns within the broader ethical context. It explores the complex ethical issues surrounding generative models, highlighting key challenges as well as new technical solutions and mitigation strategies. 

By providing a holistic overview, this review seeks to contribute to the discourse on responsible AI development, fostering a deeper understanding of how to build
equitable and trustworthy generative AI systems for the future.