A new approach called Approximate Inverse Model Explanations (AIME) offers intuitive explanations for machine learning (ML) and artificial intelligence (AI) models, bridging the gap between humans and AI. Unlike existing methods, AIME estimates an inverse operator to assess the impact of local and global features on model outcomes. Additionally, it introduces a similarity distribution plot for visualizing the target dataset’s complexity. Experiments demonstrate AIME’s effectiveness and robustness in handling multicollinearity, leading to simpler and more understandable explanations.