Shaivya Patel

How can businesses circumvent the risk of algorithmic bias in predictive AI models used for hiring decisions?

Started Jan. 22, 2024

Abstract or project description

This project focuses on algorithmic bias in hiring algorithms. It outlines the basics of algorithmic bias, explores their causes, and presents real-world examples of bias in hiring algorithms. The paper highlights the far-reaching impacts of algorithmic bias on diversity, legal and ethical considerations, as well as the potential financial repercussions for businesses. Additionally, it provides a structured analysis of potential solutions, ranging from data collection and machine learning techniques to HR training and hiring transparency. Furthermore, the project will cover successful case studies of companies that have successfully dealt with algorithmic bias in their hiring processes, as well as lessons that we can learn from previous failures. Ethical concerns and existing and emerging regulations will also be covered. The objective of this project is to provide practical and effective recommendations that businesses can implement to eliminate algorithmic bias during the hiring process. By combining my findings with practical insights, these recommendations will aim to create a balanced and fair hiring system for companies to utilize.