Machine Learning (ML) systems are frequently evaluated and optimized for predictive performance, yet real-world deployment increasingly requires simultaneous attention to fairness and interpretability. These requirements introduce objectives that can conflict in both theory and practice: Improving accuracy can exacerbate disparities across protected groups; enforcing fairness constraints can reduce utility or shift error burdens; and enhancing interpretability can restrict hypothesis classes or encourage post-hoc explanations whose fidelity is uncertain. This article treats this as a Multi-Objective Optimization (MOO) problem, emphasizing Pareto optimality, the structure of trade-offs, and decision-making on the resulting Pareto set. We review core fairness definitions and metrics, interpretability concepts and pitfalls, and MOO methods used to manage competing objectives, such as including scalarization, constrained learning, and evolutionary approaches. We then propose an evaluation and reporting framework centered on transparent visualization of trade-offs (pareto fronts and fairness-utility curves), careful metric selection (including dominance-based indicators), and documentation practices inspired by model reporting standards.