Projects rooted in artificial intelligence (AI) are fast becoming an integral part of the modern technological paradigm, aiding in decision-making processes across various sectors, from finance to healthcare. However, despite the significant progress, AI systems are not without their flaws. One of the most critical issues faced by AI today is that of data biases, which refers to the presence of systemic errors in a given set of information leading to skewed results when training machine learning models.
As AI systems rely heavily on data; the quality of the input data is of utmost importance since any type of skewed information can lead to prejudice within the system. This can further perpetuate discrimination and inequality in society. Therefore, ensuring the integrity and objectivity of data is essential.