EPIC Files Amicus Brief Challenging Spirit Airlines’ Bankruptcy Sale of Employee Data to Google

On Thursday, EPIC, along with Professor Seema Patel, filed an amicus brief using privacy grounds to challenge the sale of sensitive employee data in Spirit Airlines’ bankruptcy proceedings to Google to be used for AI training. The brief was filed with Harvard Law School’s Cyberlaw Clinic’s representation.  

Spirit Airlines, through the bankruptcy proceedings, is proposing to sell Google troves of data for $10 million. The data at issue includes employee data, such as payroll, training, discipline, travel, and internal communications, with some of the data going back decades. The Association of Flight Attendants-CWA, AFL-CIO has filed a limited objection to the sale of employee data. EPIC’s amicus brief supports this objection, highlighting the privacy issues at stake and the insufficiency of the privacy protections in the proposed sale.

Specifically, the amicus brief argues that the sale agreement does not clearly state that it will protect employee data, only consumer data. Further, even if employee data goes through deidentification, it is not a magic wand that addresses the privacy concerns around identifying employees through inferences that can be made from the mass of data that will be sold. Lastly, this sale would create incentives for employers to over-collect as much employee data as possible, in the hopes of monetizing that data in bankruptcy. Workers have little say or recourse for either the initial collection or potential misuse of that data. The brief asks the court to consider the privacy issues at stake and ensure that there are stronger privacy protections for the Spirit Airlines employees’ data. 

This amicus brief is the latest in EPIC’s Worker Privacy work. In July 2026, EPIC, along with National Institute of Workers’ Rights (NIWR), published a report, Who’s Got the Power? Restoring Worker Power in the Age of Algorithmic Unionbusting, which examines how employers increasingly use surveillance technologies, algorithmic management systems, and vast stores of worker data to identify, monitor, and interfere with workers seeking to organize.