Pseudonymization is the practice of replacing personal identifiers in data with artificial labels or codes, called pseudonyms. This process makes it harder to directly connect the information to a specific person unless additional, separately stored information is used to reverse the substitution. Unlike Anonymization, pseudonymization can be undone under controlled conditions, which means it protects privacy but does not completely remove the risk of being identified.
The significance of pseudonymization in AI ethics and law lies in its role as a safeguard for human dignity and privacy. By limiting direct identification, it reduces the chance of personal harm when sensitive data is processed, shared, or analyzed. However, because it is reversible, the technique requires strong rules of Data Governance and security to ensure that the keys linking pseudonyms to real identities are protected. Failure to do so can expose people to discrimination, surveillance, or exploitation.
From an ethical standpoint, pseudonymization should never be treated as a final guarantee of safety. It is a useful privacy measure, but it still demands accountability, transparency, and respect for the rights of individuals. In AI systems that use vast amounts of personal data, the misuse or weak application of pseudonymization is not only irresponsible but wrong. Protecting people’s privacy must come before maximizing data utility for profit or convenience.
Our global network of contributors to the AI & Human Rights Index is currently writing these articles and glossary entries. This particular page is currently in the recruitment and research stage. Please return later to see where this page is in the editorial workflow. Thank you! We look forward to learning with and from you.