From the past, we learn that these systems are brittle and break continuously. For example, what happens when spammers start using different words, or send legitimate looking emails that are actually spam? Do you think you can build rules to catch 70%, 80%, 90% or 99.99% of spam?

If your goal is simply showing the rules being applied, you can still learn the rules with ML but display them in this way (for example GP suggested looking at Naive Bayes which was the most common method used to fight spam; I'd also point you to decision trees which are easy to visualize).