Let's say that I wanted to use machine learning to find and redact personally identifying information (PII) from millions of records with open text fields.
Let's also say the PII could include a wide array of information categories like full names, dates of birth, places of residence, driver's license numbers, passports, family member names, etc.
Let's not forget to mention that thousands upon thousands of collectors were used to input this data too, which introduces an additional layer of complication (e.g. inconsistent reporting, nuanced terminology, abbreviations, spelling errors).
And finally, let's say that this data is meant to be released to the public so the risks of re-identification for nefarious purposes are very high.
Given that background, how feasible would it be to apply machine learning here? What important limitations/considerations should be taken during this process? Can machine learning address the problem of PII in this case? Why or why not?