Privacy and confidentiality expectations are bound up in cultural norms, and other aspects of particular contexts, with emerging challenges regarding potential for re-identification of participant we use participant to refer to those choosing to participate in research, those where consent-waivers may be in place or where some stakeholders may fulfil participant-researcher roles (e.g., teachers), and those 'data subjects' whose data is used in research often without their knowledge. data. Data quality, integrity, and governance are a consideration of respect for persons. - *Data quality *represents the reliability and validity of the data, key features in respecting persons through how we represent them. - *Data integrity *represents the security of the data from manipulation or corruption, ensuring data can be used for its purpose.“Threats to privacy are posed by AI systems both as a result of their design and development processes, and as a result of their deployment. As AI projects are anchored in the structuring and processing of data, the development of AI technologies will frequently involve the utilisation of personal data. This data is sometimes captured and extracted without gaining the proper consent of the data subject or is handled in a way that reveals (or places under risk the revelation of) personal information. On the deployment end, AI systems that target, profile, or nudge data subjects without their knowledge or consent could in some circumstances be interpreted as infringing upon their ability to lead a private life in which they are able to intentionally manage the transformative effects of the technologies that influence and shape their development. This sort of privacy invasion can consequently harm a person’s more basic right to pursue their goals and life plans free from unchosen influence.” (Leslie, 2019, p. 5)