Measuring Data Quality for Project Lighthouse

Adam Bloomston; Elizabeth Burke; Megan Cacace; Anne Diaz; Wren Dougherty; Matthew Gonzalez; Remington Gregg; Yeliz Güngör; Bryce Hayes; Eeway Hsu; Oron Israeli; Heesoo Kim; Sara Kwasnick; Joanne Lacsina; Demma Rosa Rodriguez; Adam Schiller; Whitney Schumacher; Jessica Simon; Maggie Tang; Skyler Wharton; Marilyn Wilcken

arXiv · 2510.06121

Measuring Data Quality for Project Lighthouse

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Abstract

In this paper, we first situate the challenges for measuring data quality under Project Lighthouse in the broader academic context. We then discuss in detail the three core data quality metrics we use for measurement--two of which extend prior academic work. Using those data quality metrics as examples, we propose a framework, based on machine learning classification, for empirically justifying the choice of data quality metrics and their associated minimum thresholds. Finally we outline how these methods enable us to rigorously meet the principle of data minimization when analyzing potential experience gaps under Project Lighthouse, which we term quantitative data minimization.

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Adam Bloomston, Elizabeth Burke, Megan Cacace, Anne Diaz, Wren Dougherty, Matthew Gonzalez, Remington Gregg, Yeliz Güngör, Bryce Hayes, Eeway Hsu, Oron Israeli, Heesoo Kim, Sara Kwasnick, Joanne Lacsina, Demma Rosa Rodriguez, Adam Schiller, Whitney Schumacher, Jessica Simon, Maggie Tang, Skyler Wharton, Marilyn Wilcken. 2025-10-07. Measuring Data Quality for Project Lighthouse. https://arxiv.org/abs/2510.06121

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