Student seeks real-world datasets for AI privacy and bias project

A student on Reddit has sparked a conversation about the scarcity of authentic datasets for privacy-focused data science projects. Their professor assigned a real-world data analysis project covering data privacy, bias, and interpretability, requiring a dataset with as little anonymity as possible. This would allow them to apply techniques like differential privacy and k-anonymity in a meaningful real-world context. The student checked Kaggle but found it difficult to verify whether datasets were genuinely collected or synthetically generated.

The post underscores a critical gap in the AI ethics research pipeline: while many synthetic or heavily anonymized datasets exist, open access to raw, privacy-sensitive records is rare due to legal and ethical constraints. For students and researchers, this limits hands-on experimentation with privacy-preserving technologies. The discussion suggests alternative sources like government open data portals (data.gov, EU data), medical datasets (MIMIC-III), or social science repositories (ICPSR). The challenge also reflects broader industry needs for benchmark datasets that balance realism with ethical compliance.