Machine learning (ML) has transformed engineering, yet competitiveness cannot be defined by accuracy and performance anymore. As ML models increasingly influence fundamental models, the focus shifts from “how well do they predict?” to “how explainable and responsibly do they act?”. This article examines the limitations of current models and evaluation approaches and highlights the ethical implications of ML. It stresses the significance of fairness, accountability, transparency, and social impact alongside performance. Through real-world examples, it is shown how overlooked design and evaluation factors can undermine trust and cause harm. Furthermore, research trends indicate a growing gap between applied and theoretical ML, with a notable delay in research on responsible ML practices. The rapid expansion of applied ML contrasts with the slow development of fairness, interpretability, and ethical concerns. This gap threatens the reliability and transparency of future ML models. The article proposes practical steps to develop ML models that are not only effective but also ethical, explainable, and aligned with societal values to ensure responsible innovation.