Enhancing Transparency and Mitigating Bias in Large Language Models' Responses with Sophistication
Swayam Gupta, Roumo Kundu, Anuj Kumar Aditya Deo, Manorama Patnaik, Tannisha Kundu, Mohan Kumar Dehury · 2024
Algorithmic bias, woven into the fabric of today's world, skews opportunities and fuels discrimination. This can have amplifying consequences in the healthcare sector, image generation, and many others. Existing techniques to mitigate bias in AI primarily focus on data cleansing and model retraining, often proving ineffective due to the inherent complexity and hidden nature of biases. The main problem lies in the algorithm used for the cleaning of the data and not being effective enough to mitigate the bias present in the data. In contrast, the proposed approach is an architectural fusion of two of the algorithms for debiasing the data namely reweighing and adversarial debiasing. This architectural fusion promises significant advancements in transparent and unbiased chatbot interactions, compared to existing methods, it is going to pave the way for fairer and more equitable communication in the age of AI.