Explainable Career Path Predictions using Neural Models

Career path prediction aims to determine a potential employee’s next job, based on the jobs they have had until now. While good performance on this task has been achieved in recent years, the models making career predictions often function as black boxes. By integrating components of explainable artificial intelligence (XAI), this paper aims to make these predictions explainable and understandable. To study the effects of explainability on performance, three non-explainable baselines were compared to three similar, but explainable, alternatives. Furthermore, user testing was performed with recruiters in order to determine the sensibility of the explanations generated by the models. Results show that the explainable alternatives perform on-par with their non-explainable counterparts. In addition, the explainable models were determined to provide understandable and useful explanations by recruiters.