The Singularity, a theoretical period of extremely fast technology development driven by artificial intelligence (AI)—offers both thrilling opportunities and moral dilemmas. Algorithmic bias is one such issue that AI-powered recruiting procedures may accelerate. Understanding scenarios where AI outperforms humans can actually be a task. In light of the Singularity, this paper examines the moral effects of biased employment algorithms and suggests an inclusive and equitable alternative. This paper illustrates the possibility for a more moral and diverse workforce by contrasting our suggested method with conventional, perhaps biased hiring practices. It proposes an inclusive and equitable alternative using imbalance-learn library, an effective tool made to deal with class imbalance in training sets. Our Sequential Neural Network reduction in bias, with the imbalance-learn method achieving a success rate of 0.7862 compared to traditional methods. The purpose of this study is to demonstrate how our suggested recruiting strategy, which is supported by the imbalanced-learn library, might aid in locating exceptionally competent applicants who biased algorithms might otherwise miss. In the end, this might result in the development of a more varied talent pool, promoting a more moral and fairer workplace in the future. This study underscores the importance of ethical AI in the age of Singularity and offers a practical solution for fostering fairness in automated hiring.