Dataset imbalances, representative bias, data mismatch etc, are all common problems that businesses face in integrating AI systems into their business models.We plan to give a comprehensive solution to solve biasness in data, using the right method to train and test a model, and using the right metrics for evaluating a model.

We also plan to make Integrated AI system that will work with human employees in their decision-making skills.By using the C4.5 algorithm, we are able to split our datasets into a decision tree. That way, groups of input data can be corresponded to the target value more easily. It also reduce and simplify our data, leading to faster training.

By using resampling techniques to remove representative bias, an algorithmic hiring model should perform better and achieve higher results for precision and recall scores, allowing us to accurately evaluate police officers for risks of police brutality.We can also use this technique in other fields to remove AI bias