Data Voids: Where Missing Data Can Easily Be Exploited

By D. boyd, M. Gołębiewski. 2018

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Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “by traditional gender roles? What are the dangers of identifying caste in models? Should instances where models are deliberately ‘confused’ (see Section 4) be identified, and if so what should we do with such data? We must also account for data voids [78] for which statistical extrapolations might b”
When Are Search Completion Suggestions Problematic? (2020, background): “Low frequency queries are more susceptible to manipulation by adversaries, and are more frequently associated with lower quality data [26].”