Limitless Worker Surveillance
Open accessAbstract
From the Pinkerton private detectives of the 1850s, to the closed-circuit cameras and email monitoring of the 1990s, to new apps that quantify the productivity of workers, and to the collection of health data as part of workplace wellness programs, American employers have increasingly sought to track the activities of their employees. Starting with Taylorism and Fordism, American workers have become accustomed to heightened levels of monitoring that have only been mitigated by the legal counterweight of organized unions and labor laws. Thus, along with economic and technological limits, the law has always been presumed as a constraint on these surveillance activities. Recently, technological advancements in several fields—big data analytics, communications capture, mobile device design, DNA testing, and biometrics—have dramatically expanded capacities for worker surveillance both on and off the job. While the cost of many forms of surveillance has dropped significantly, new technologies make the surveillance of workers even more convenient and accessible, and labor unions have become much less powerful in advocating for workers. The American worker must now contend with an all-seeing Argus Panoptes built from technology that allows for the trawling of employee data from the Internet and the employer collection of productivity data and health data, with the ostensible consent of the worker. This raises the question of whether the law still remains a meaningful avenue to delineate boundaries for worker surveillance. In this Article, we start from the normative viewpoint that the right to privacy is not an economic good that may be exchanged for the opportunity for employment. We then examine the effectiveness of the law as a check on intrusive worker surveillance, given recent technological innovations. In particular, we focus on two popular trends in worker tracking—productivity apps and worker wellness programs—to argue that current legal constraints are insufficient and may leave American workers at the mercy of 24/7 employer monitoring. We consider three possible approaches to remedying this deficiency of the law: (1) a comprehensive omnibus federal information privacy law, similar to approaches taken in the European Union, which would protect all individual privacy to various degrees regardless of whether or not one is at work or elsewhere and without regard to the sensitivity of the data at issue; (2) a narrower, sector-specific Employee Privacy Protection Act (EPPA), which would focus on prohibiting specific workplace surveillance practices that extend outside of work-related locations or activities; and (3) an even narrower sector and sensitivity-specific Employee Health Information Privacy Act (EHIPA), which would protect the most sensitive type of employee data, especially those that could arguably fall outside of the Health Insurance Portability and Accountability Act’s (HIPAA) jurisdiction, such as wellness and other data related to health and one’s personhood.
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US literature on bias, especially racial bias, in the algorithmic workplace focuses on hiring because the US in general has at will firing with few legal constraints.
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- C.F.R. pts. 146-47). 166. TOWER WATSON/NAT'L BUS. GRP. ON HEALTH, THE NEW HEALTH CARE IMPERATIVE: DRIVING PERFORMANCE, CONNECTING VALUES 6 (2014), https://www.towerswatson.com/en- US/Insights/IC-Types/Survey-Research-Results/2014/05/full-report-towers-watson-nbgh-2013-2014- employer-survey-on-purchasing-value-in-health-care [https://perma.cc/M73X-W4AM]. 167. Id. 168. Id. 169. REDBRICK HEALTH, PATIENT PROTECTION AND AFFORDABLE CARE ACT OF 2010 (ACA) WELLNESS RULES 6 (2013). 170. David Blumenthal, Employer-Sponsored Health Insurance in the United States-Origins and Implications, 355 NEW ENG. J. MED. 82, 82 (2006). 171. John Cawley, The Affordable Care Act Permits Greater Financial Rewards for Weight Loss: A Good Idea in Principle, But Many Practical Concerns Remain, 33 J. OF POL'Y ANALYSIS & MGMT. 810, 811 (2014). 172. Sharon Begley, Employer Incentives for U.S. Worker Wellness Set Record, REUTERS (Mar. 26, 2015 4:13 AM), http://uk.reuters.com/article/2015/03/26/us-usa-healthcare-wellness- idUKKBN0MM0BB20150326 [https://perma.cc/N3A8-RZ7Q]; see also Kate Crawford et al., Our Metrics, Ourselves: A Hundred Years of Self-Tracking from the Weight Scale to the Wrist Wearable Device, 18 EUR. J. CULTURAL STUD. 479, 486 (2015). 173. Begley, supra note 172. 174. Most users wear fitness trackers on their wrist, and the trackers use accelerometers to measure motion. Some inherent flaws are that the accelerometers measure only motion, not exertion, and that some of "today's wrist-worn accelerometers are still calibrated for steps." Albert Sun & Alastair Dant, What Your Activity Tracker Sees and Doesn't See, N.Y. TIMES (Mar. 11, 2014), https://www.nytimes.com/interactive/projects/well/2014/03/accelerometers.html [https://perma.cc/2ZZF-EEGG]. This means that the trackers cannot tell when an individual is cycling and thus will not count that as physical activity. See id. 175. See Hannah Augur, The Physician and the Fitbit: Why Doctors and Administrators Don't Love Wearables, DATACONOMY (Mar. 29, 2016), http://dataconomy.com/the-physician-and-the-fitbit- why-doctors-and-administrators-dont-love-wearables-2 [https://perma.cc/5QMB-TUTN]. 176. Many wellness programs use body mass index (BMI) as a metric to determine obesity. However, current medical research shows that this is an inaccurate metric since BMI does not distinguish between fat mass and muscle mass. See Alban De Schutter, et al., Body Composition and Mortality in a Large Cohort with Preserved Ejection Fraction: Untangling the Obesity Paradox, 89
- MAYO CLINIC PROC. 1072, 1077 (2014).
- See also Albert Sun, Same B.M.I., Very Different Beach Body, N.Y. TIMES (Sept. 3, 2015), http://www.nytimes.com/interactive/projects/cp/summer-of- science-2015/latest/bmi?smid=tw-nytimes [https://perma.cc/8JT3-77C6]. 177. See Kate Crawford, When Fitbit is the Expert Witness, ATLANTIC (Nov. 19, 2014), http://www.theatlantic.com/technology/archive/2014/11/when-fitbit-is-the-expert-witness/382936 [https://perma.cc/283Z-LEZE]. 183. See Complaint ¶ 16, EEOC v. Orion Energy Sys. (E.D. Wis. 2016) (No. 1:14-cv-01019- WCG), 2014 WL 4180675, at *1. 184. See id. 185. EEOC Opening Brief at 4-7, 9-10, EEOC v. Flambeau, Inc., 131 F.Supp.3d 849 (W.D. Wis. 2014) (No. 16-1402). 186. Id. 187. Id. 188. Id. 189. See id.; Complaint ¶ 11, EEOC v. Flambeau, Inc., 131 F.Supp.3d 849 (W.D. Wis. 2014) (No. 3:14-cv-00638-bbc). 190. See EEOC Regulations Under the Americans with Disabilities Act, 29 C.F.R. § 1630 (2016) (final rule on employer wellness programs and Title I of the ADA). on large data sets to identify patterns in order to make economic, social, technical, and legal claims. (3) Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy." See danah boyd & Kate Crawford, Critical Questions for Big Data, 15 INFO. COMM. & SOC'Y 662, 663 (2012). 195. See, generally, Émile DURKHEIM, THE DIVISION OF LABOUR IN SOCIETY (1893). 196. Dougherty & Hardy, supra note 47. 197. Id. 198. Id. 199. Shoshana Zuboff, Big Other: Surveillance Capitalism and the Prospects of an Information Civilization, 30 J. INFO. TECH. 75, 89 (2015). 200. Cohen, supra note 13, at 207. 201. Steve Lohr, Big Data, Trying to Build Better Workers, N.Y. TIMES (April 20, 2013), http://www.nytimes.com/2013/04/21/technology/big-data-trying-to-build-better-workers.html [https://perma.cc/J5PA-XD9W]. 202. Id. 203. Id. 204. Cohen, supra note 13, at 207. 205. HORACE BOOKWALTER DRURY, SCIENTIFIC MANAGEMENT: A HISTORY AND CRITICISM 15-21 (Colum. Univ. 2d ed. 1918) (1915). 206. Id. 207. See FREDERICK WINSLOW TAYLOR, THE PRINCIPLES OF SCIENTIFIC MANAGEMENT 11- 13 (1911). 209. HIPAA has jurisdiction over health information handled by health care providers. The law is not settled on whether wellness program vendors fall within that category. The U.S. Department of Health and Human Services provides an explanation regarding which entities are covered under HIPAA and that list does not explicitly include wellness programs: "The HIPAA Privacy Rule establishes national standards to protect individuals' medical records and other personal health information and applies to health plans, health care clearinghouses, and those health care providers that conduct certain health care transactions electronically." See The HIPPA Privacy Rule, U.S. DEP'T OF HEALTH AND HUMAN SERVS., (emphasis added) https://www.hhs.gov/hipaa/for- professionals/privacy/index.html?language=es [https://perma.cc/9BQ4-E3RW]. 210. See Scot Ganow & Sam S. Han, Model Omnibus Privacy Statute, 35 U. DAYTON L. REV. 345 (2010); see also Wilborn, supra note 123, at 862 ("One of the more extreme proposals suggested to solve the problem of employee privacy, at least with respect to electronic monitoring and surveillance, has been Professor Laurence Tribe's proposal of a Twenty-Seventh Amendment to the United States Constitution");
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- Authors
- Ifeoma Ajunwa, K. C.
- Venue
- California Law Review
- Publisher
- California Law Review
- Published
- 2017
- OpenAlex
- W3124426735
- Cited by
- 241
- References
- 0
- Citations by year
- 2026: 11 · 2025: 22 · 2024: 45 · 2023: 31 · 2022: 15 · 2021: 23 · 2020: 37 · 2019: 36 · 2018: 13 · 2017: 5 · 2016: 2
- FWCI
- 188.46
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- UC Berkeley
- Topics
- Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
- Social Sciences › Sociology and Political Science › Law, Rights, and Freedoms
- Social Sciences › Political Science and International Relations › Labor Law and Work Dynamics
- Keywords
- worker surveillance, employee monitoring, employee privacy, productivity apps, biometrics, DNA testing, mobile device tracking, data analytics, employer monitoring, privacy law
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