Bias detection and mitigation in AI algorithms: A comprehensive study

Abstract

This research is about bias in intelligence (AI) systems. It tries to find ways to make AI fairer.The study looks at areas like hiring, finance and healthcare where AI is used. These areas are important. We do not want AI to make existing problems worse. The researchers use causal methods to find bias in AI models. They look for bias in AI systems. They then try to reduce bias using techniques. These techniques include pre-in-processing and post-processing methods. The researchers create a framework to make AI fairer. They use measures like parity equalized odds and counterfactual fairness. Their results show that AI can be fair without losing performance. This makes their approach useful, for real-world use and future expansion.The study aims to make AI systems fairer and more transparent.It also wants to ensure AI is used ethically.