This study examines the effect of a biased dataset in the Artificial Intelligence (AI) learning process. Biases are almost inevitable and history has shown that it may significantly influence AI models, leading potentially to erroneous results and even dangerous consequences. We propose, through the case of air traffic control, an evaluation of the influence of bias in AI model training and performance. Air traffic control requires years of operational experience to master the environment and develop effective conflict resolution strategies. The level of experience is known to influence significantly the aircraft collision avoidance strategy. Our study is based upon a dataset collected with controllers of various levels of experience, leading to the creation of a bias in their avoidance strategy, the validation of this bias through statistics and the AI model development and simulation. Our approach compares two iterations of the model; one without any action to handle the bias and one by integrating it as a feature. The main findings show that declaring the bias as a feature does not necessarily impact AI model learning and overall accuracy, but can clearly influence the results over specific classes. The results of the second iteration contributing to higher alignment with specific preferences of the experienced controllers, versus the novice ones.