The areas of safety and robustness are key areas where communities from verification, neuro-symbolic AI, and machine learning come together. Safety and robustness are often formalized in terms of point-wise metrics: given an input point, we identify a circle or a region where certain properties hold in terms of the consistency of prediction. However, the broader goal of neuro-symbolic AI applied to machine learning correctness would ideally integrate safety and robustness conditions with explanations. Nonetheless, there is no paper that discusses these properties in a unified manner. What we consider in this paper is a new simple framework for formalizing a variety of such properties. We are able to characterize the robustness condition, safety conditions, hyper-safety conditions, counterfactual explanations, and fairness, among others. We can express these properties using simple notation for an abstract model based on a binary classifier. We hope these definitions would lead to neuro-symbolic frameworks that contribute to all of these areas jointly.