From Bias to Balance: Addressing Bias and Fa...

From Bias to Balance: Addressing Bias and Fairness in AI Algorithms

Author Name : Vansh Thirani

ABSTRACT This document provides a detailed analysis on Bias and Fairness in AI Algorithms in the real world. Algorithmic bias occurs when AI systems reflect societal biases embedded in their training data, leading to potential discrimination in certain areas like housing, hiring etc. Factors such as unbalanced datasets, feedback loops, and malicious manipulation exacerbate these issues, creating unfair or harmful outcomes.To address these challenges, greater transparency, robust oversight, and careful scrutiny of AI systems are essential. While these measures face obstacles like privacy concerns and the complexity of deep learning, fostering awareness and advocating for ethical AI practices can help mitigate bias. Developing standardized guidelines and testing frameworks may ensure fairness and build trust in AI technologies.