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The Golem in the Machine: FERPA, Dirty Data, and Digital Distortion in the Education Record


Najarian R. Peters


March 4, 2022

Like its counterpart in the criminal justice system, dirty data—data that is inaccurate, incomplete, or misleading—in K-12 education records creates and catalyzes catastrophic life events. The presence of this data in any record suggests a lack of data integrity. The systemic problem of dirty data in education records means the data stewards of those records have failed to meet the data integrity requirements embedded in the Family Educational Rights and Privacy Act (FERPA). FERPA was designed to protect students and their education records from the negative impact of erroneous information rendered from the “private scribblings” of educators. The legislative history of FERPA indicates that legislators were concerned about the harm to students’ education and the structure of opportunities based on misinformation in secret files created and kept in schools. Dirty data created, collected, and processed as accurate and reliable, notwithstanding the disproportionate impact of school discipline, on marginalized students in general, and Black children specifically, is exactly the kind of harm that FERPA was intended to prevent. This Article demonstrates (1) how educational inequities linked to dirty data implicate student privacy interests understood at the time FERPA was created; and (2) how FERPA should be enhanced to prevent dirty data harms at the point of collection and creation. Additionally, this Article outlines the concept of dirty data and data integrity requirements embedded in FERPA and proceeds to examine the phenomenon of dirty data and student harm in historically marginalized students’ education records, starting at the point of creation and collection. While several Articles have examined the failure of FERPA, none of the prior scholarship has analyzed FERPA’s connection to dirty data in the education record related to racial discrimination. This Article introduces a two-step process that would require input validation in the educational record context through (1) substantive content and input validation; and (2) a reasonable inference review. Finally, this Article introduces a requirement of accounting of disclosures to law enforcement.


Najarian R. Peters, The Golem in the Machine: FERPA, Dirty Data, and Digital Distortion in the Education Record, 78 Wash. & Lee L. Rev. 1991 (2022).