Fairness interventions are a key focus in most Artificial Intelligence (AI) ethics research fields. When biases related to some features (e.g., race, sex, age, religion) are identified in AI systems that contribute to discrimination outcomes, developers, engineers, or stakeholders must choose how and when to intervene. However, given the plethora of available options, a lack of standardization in the intervention process prevails, making it challenging to determine the suitable option for a given context. In this work, we propose a developmental framework to explore different types of measures based on non-discrimination criteria aimed at filling the gap between AI developers and social practitioners . We then construct a framework to analyze the performance of the interventions over AI models in terms of statistical non-discrimination fairness criteria.