Fairness and bias in AI for human resource selection and promotion are complex issues that require careful and continued attention. While AI has the potential to improve equity by eliminating subjective human bias, it also presents significant risks if inherent biases in data and algorithms are not adequately addressed. By prioritizing equity, transparency and diversity, we can harness the power of AI to promote fairer and more inclusive work environments. This article introduces the Evaluative Customized Naïve Associative Classifier as an alternative to promote equity and transparency in the automatic selection and promotion processes of human resources. The results obtained support the viability of the proposal within the Evaluative Artificial Intelligence approach.