Authors: Payal Anil Barhate, Associate Professor Dr. Ayesha Siddiqui, Dr.Nagsen Bansod, Dr.Rajkumar Deshpande
Abstract: Artificial Intelligence (AI) is changing the way companies hire people by making it possible to automatically screen resumes, predict who will get the job, and assess behaviour. These new technologies make things much more efficient and scalable, but they also raise important ethical issues, especially the possibility of algorithmic bias. Unfair discrimination against candidates based on their gender, race, or socioeconomic background can happen when training data is biassed, decision-making models are unclear, and there is no accountability. This study looks into where bias comes from and how it shows up in AI-powered hiring systems. It also looks at a range of ways to reduce bias, which are divided into three groups: pre-processing, in-processing, and post-processing. It looks at how well tools like fairness-aware learning, adversarial debiasing, and equalised odds post-processing work to promote fairness.
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