Could a faulty data system be the difference between food and starvation? David Loshin explores real-world examples of data matching errors, including a case in India where algorithmic errors led to the denial of food benefits. He and Doug get into data identity resolution, anonymization (and de-anonymization), and the risks associated with misidentification in large datasets. Learn more about the challenges of maintaining data privacy while achieving accurate record linkage and the potential role of AI in improving these processes. References: • How an algorithm denied food to thousands of poor in India’s Telangana • How We Investigated Welfare Algorithms in India (Part I) • Health Data in an Open World • How small details can create a big problem • Re-Identification of “Anonymized” Data • Revisiting the Uniqueness of Simple Demographics in the US Population • Simple Demographics Often Identify People Uniquely

Information Risk is a podcast hosted by Doug Atkinson featuring David Loshin, a renowned data strategy consultant and director at the University of Maryland. David explores theoretical and real-world examples of 'information risk', the different types of risk that misuse and misinterpretation of data can pose to organizations. Contact David Loshin: ⁠⁠⁠⁠LinkedIn⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠knowledge-integrity.com⁠⁠⁠⁠ Powered by ⁠⁠⁠⁠Insight Jam⁠