One of the ethical concerns in AI is the bias that could exist in a deep-learning algorithm. There are some possible causes of bias in AI, such as inappropriate training data selected, and unintentional input of unethical values into systems that could be a known or an unknown existing prejudice. Some common prejudices include race, gender, sexual orientation, and socioeconomic status. The ideal way to remove bias completely in the model is to provide sufficient and diverse data SELECTION to train the AI algorithm. Unfortunately, it is almost impossible to find a perfect fit as ‘unconscious’ biases exist in the free society. In the process of debasing datasets, many data are extracted out and the product may become less useful. Sometimes, you will not even be able to uncover the ‘unconscious’ bias until you get the AI algorithms in. Thus, researchers and analysts have to slowly reveal them, it is a process ongoing which takes time and effort to produce a fairer result.