FROM BIAS TO BRILLIANCE: THE PROMISES AND CHALLENGES OF AI IN LITERATURE

Abstract

This study examines the dualistic role of Artificial Intelligence (AI) in literary analysis, showcasing its groundbreaking potential alongside inherent challenges. While AI democratizes access to advanced analytical tools and enhances efficiency, it faces significant barriers such as algorithmic bias and limitations in interpreting cultural, emotional, and psychological nuances. Traditional literary analysis is a subjective approach in that it draws from the reader's experience and culture in seeking deeper meanings, themes, and emotions in a piece. Although this technique may bring forth more insightful ideas, it is always hampered by human bias, lack of time, and inconsistencies. With its advanced Normal Dialect Preparing and machine learning capabilities, AI has the potential to convert the field by analyzing phenomenal volumes of content, recognizing designs, and recognizing topics. It can encourage estimation examination, complex comparisons, and authentic contextualization, democratizing get to to modern scholarly apparatuses. The study finds that there are significant barriers such as algorithmic biases found within biased training datasets and an inability of AI to relate at the emotional, psychological, and cultural levels of literature. The study looks into AI's tendency to reflect societal inequalities, such as gender bias and nonrepresentational of non-Western voices, marginalizing certain authors and traditions. AI also fails at the internationalization of identities and the complexities of human experiences portrayed in literature. Observations reveal how AI focuses on linguistic patterns, which often takes the place of holistic understanding. The study calls for diversified and inclusive datasets, an enhanced AI system that can detect inter-sectional identities, and tools for bias detection. Although AI cannot replace human interpretation, it has much potential as a complementing tool in literary analysis. The study concluded that the incorporation of AI's analytical efficiency with human insight could well create a more equitable and comprehensive approach to understanding literature.

Keywords: Intersectionality, Algorithmic Bias, Semantic Analysis, Literary Inclusivity, AI assisted interpretation.