Transparency in Translation: A Deep Dive into Explainable AI Techniques for Bias Mitigation
Ruchira Deokar, Preethi Nanjundan, Sachi Nandan Mohanty · 2024
In an era dominated by artificial intelligence (AI), concerns about bias and discrimination loom large. The quest for fairness and equity in AI-driven decision-making has led to the exploration of Explainable AI (XAI) as a viable solution. This paper undertakes a thorough examination of the bias ingrained within AI systems and posits XAI as a potent antidote. Beginning with an exploration of the origins and aftermath of bias in AI, the analysis traverses the evolution of XAI techniques, including SHAP, LIME, and counterfactual explanations, clearly stating their advantages and drawbacks. With each XAI method thoroughly inspected, the study unravels their applicability across diverse AI models and domains. Furthermore, a compelling case study is presented, showcasing XAI's practical application in a language translation app, where it guarantees transparency and equity in the translation process. This tangible example serves as a testament to XAI's efficacy in mitigating bias within real-world applications. As the analysis concludes, it underscores the pivotal role XAI plays in fostering accountability and trustworthiness in AI systems. By shedding light on how XAI mitigates bias and offering concrete examples of its utility, the paper advocates for its widespread adoption as an imperative step towards the development of ethically robust AI systems. In a landscape filled with concerns about bias, XAI emerges as a beacon of hope, promising a future where AI decisions are transparent, fair, and equitable for all.