The use of large datasets, often containing sensitive personal information, presents significant privacy risks in AI development and application. Ensuring the confidentiality and security of this data is not just a technical requirement – it is a fundamental ethical responsibility that comes with technological challenges Feretzakis, 2024.
Without robust safeguards, personal information could be misused or exposed, eroding trust and causing harm. In research fields where scientists routinely handle complex datasets that may include proprietary, sensitive experimental results or patients’ data, these privacy risks are amplified, potentially undermining both scientific integrity and public trust.
In cases when the patients’ data is involved, AI privacy risk issues need to be addressed by large multidisciplinary teams and consortia involving healthcare providers at all levels, AI developers from both academia and industry, ethics specialists, and policymakers Yekaterina, 2024. Otherwise, creating robust, privacy-preserving AI systems in healthcare does not seem feasible, as this is not a problem that can be solved single-handedly.
At scienceOS, we acknowledge that building AI systems that respect privacy and protect data is essential for maintaining ethical standards in research and beyond. We are committed to data protection. By design, we follow the principles of minimal data collection, need-to-know, and need-to-have to ensure data privacy.
