Title: Towards fairness AI: A data-centric approach Subtitle: Assessment and mitigation techniques to tackle bias in datasets The consequences of bias and injustice have received more attention, even though AI is increasingly employed in delicate fields like health care, hiring, and criminal justice. We know that individual and social biases, which are frequently unconscious, affect and skew human decision-making in many ways. Although it might seem that using data to automate judgments would guarantee fairness, we now know that this is untrue. Societal bias can be incorporated into training datasets for AI, decisions made even during the machine learning development stage, and intricate feedback loops that form when a machine learning model is used in the real world.