Biometric technology and wearable technology such as AR glasses, iris and face scanning technologies, and wristbands collect, process, and store physiological data. Surveillance is inherently built within these technologies and their data processing, as well as their potential contributions to imbalances in the workplace.

Biometric technologies are mainly used for identity management, or how to ensure that company policies are followed and corporate privacy is secure. However, employees do not have easy access to what data collection is occurring, as well as their safety. If hacking occurs, or if companies choose to use this data for unintended purposes, employees will be unaware or will not have any agency to fix it. Identity theft becomes a probable harm for employees through this data.

Wearable technologies, on the other hand, promote the constant collection of data both during work and non-work hours. With monitoring at all hours of the day, especially with wristbands like FitBits, employees do not retain any privacy from their employees. Although the specifics of which data is being used by companies is not clear, there is a large potential risk for employees. Furthermore, because wearable technologies collect data constantly from free-living environments, they can provide information on trends or patterns of employees. This can lead to the standardization of what productivity means in the workplace, allowing the computerizing this physiological data collection to rule over employees' abilities, rather than their own human experiences.

Dunstone, T., & Yager, N. (2008). Biometric System and Data Analysis: Design, Evaluation, and Data Mining. Springer Science & Business Media

Godfrey, A., Hetherington, V., Shum, H., Bonato, P., Lovell, N. H., & Stuart, S. (2018). From A to Z: Wearable technology explained. Maturitas, 113, 40-47. https://doi.org/10.1016/j.maturitas.2018.04.012

Mekruksavanich, S., & Jitpattanakul, A. (2021). Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models. Electronics, 10(3), 308. https://doi.org/10.3390/electronics10030308