Redefining Productivity in the Age of Workplace Surveillance

Human Rights Research Center
Jul 24
6 min read

Author: Emma Nelson

July 24, 2025

[Image Source: Surveillance can have “chilling effect” on Black workers’ rights - International Employment Lawyer]

What counts as productivity has always been contested, but increasingly, it’s being defined by machines. In contemporary workplaces, surveillance technologies have evolved beyond the occasional glance over a cubicle wall by a manager into a complicated infrastructure of control. Many employers have begun tracking screens, logging keystrokes, analyzing internal communications, and even scanning employees’ faces for signs of fatigue. Today, 74% of employees use online monitoring tools, encouraged by the growth of remote work since the COVID-19 pandemic. 67% of employers currently use biometric verification as part of their measures, and 61% use artificial intelligence (AI) systems to assess worker performance. The result is a workplace where privacy is a selective privilege rather than a given, contingent on metrics of productivity and trust.

The technology used ranges from AI-powered systems monitoring the tone of internal communications to programs that track the frequency of messages and log application use across digital platforms. Things like task completion, web browsing, and response times are filtered through algorithms to determine productivity scores. Some systems go even further, conducting automated background checks that scrape public records, financial data, and social media activity to assess risk for the workplace. These evaluations are typically automated and unreviewable by humans, done entirely by systems whose logic is hidden behind code while they spew out an analysis on a person’s feasibility as an employee.

Alongside monitoring productivity in terms of output, emotional monitoring has emerged as a subset of workplace AI, claiming to assess mood and cognitive state based on facial expression and biometric feedback such as heart rate. As early as 2019, over half of large U.S. employers had adopted some form of emotional AI in the workplace, from customer service bots trained to detect caller frustration to facial expression analysis software used in hiring. These systems now influence decisions about hiring, promoting, and firing, areas that previously relied on human discretion and reasoning, holistically looking at context. Now, workers are increasingly subject to automated judgments based on their perceived emotional state, with little clarity on how such determinations are made or how they might be contested.

These surveillance systems are frequently defended under the guise of efficiency and corporate accountability. Yet the evidence that surveillance improves organizational performance is limited. In fact, research suggests that constant monitoring often undermines productivity, particularly in collaborative and creative environments where psychological safety and freedom are key to inspiration and innovation. Rather than fostering genuine engagement, AI surveillance incentivizes the performance of work even when genuine productivity is lost. Employees, aware that their actions are being logged, may resort to gestures of activity, such as moving their mouse every so often, to signal productivity to an algorithm rather than to produce meaningful work.

The psychological and cultural impacts of surveillance extend beyond individual stress to reshape workplace dynamics at large. Constant monitoring creates a “chilling effect,” which refers to an unwillingness to engage in open dialogue or dissent for fear of repercussions. In environments where every keystroke or facial expression can be tracked, employees are less likely to question decisions, propose unconventional ideas, or engage in collaborative risk-taking. The fear of leaving a digital trace, even when feedback is technically allowed, leads to self-censorship and harms a sense of community in the workplace. These dynamics can be observed outside of professional spaces as well, with 60% of students reporting discomfort expressing honest opinions when they know they are being digitally surveilled.

Exposure to such systems on a daily basis can also contribute to burnout, rampant anxiety, and a lack of satisfaction with one’s career. The constant pressure to appear productive and avoid missteps brings about psychological strain that impacts workers’ sense of identity and belonging. When people are reduced to metrics like keystroke counts or biometric compliance, they come to see themselves as thought of as data points rather than skilled contributors.

Importantly, these harms are not evenly distributed. Workers in low-wage or informal sectors such as delivery services, warehouse roles, and assignment-based platforms are subjected the most to AI surveillance. Algorithmic management systems in these roles often set unrealistic productivity targets and mark small deviations. For workers in these environments, surveillance affects scheduling, pay, access to shifts, and even whether one remains employed. The consequences are especially felt by racialized and marginalized communities who often feel the effects of AI systems trained on biased datasets that amplify and replicate discriminatory outcomes.

[Image Source: The Ethics of Employee Surveillance: Privacy Vs. Productivity - Crescentia Global Talent Solutions, LLC]

Despite these widespread concerns, legal protections against AI workplace surveillance remain disjointed. In the United States, there is no comprehensive federal framework regulating these systems at all. Many jurisdictions permit invasive monitoring without meaningful consent, and few offer workers a viable way to challenge decisions made by algorithmic systems. The way AI operates internally, without always leaving a clear trace of how it arrives at a conclusion, places accountability in a complicated state. Employees are unable to understand how decisions are made or access the data used to evaluate them.

By contrast, the European Union has taken a more assertive approach to AI regulation. The EU AI Act, set to become law in 2026, classifies employer use of AI as “high-risk,” banning practices such as workplace emotion recognition and mandating transparency and human oversight for all surveillance systems. Employers found in violation of the law may face penalties as high as €35 million or 7% of their global revenue. While regulatory frameworks may begin to limit the most invasive practices, the broader shift in how labor is organized under AI systems continues to unfold, often without clear visibility or public debate.

AI monitoring systems don’t just observe work, but rather quietly restructure it. Surveillance technologies assign value based on visibility and compliance, filtering human behavior through metrics that strip away context and limit nuance. Over time, this redefines what counts as productivity and what kinds of workers are seen as trustworthy or efficient. As AI tools become more embedded in routine management, they start to shape not just outcomes but also expectations about what a “good worker” looks like, how much friction is tolerated, and how judgement gets replaced by scores. Surveillance becomes less about identifying workplace misconduct and more about preemptively designing it out, automating not only oversight, but obedience.

Glossary

AI (Artificial Intelligence) - Technology designed to perform tasks that typically require human intelligence, such as decision-making, pattern recognition, or emotion detection
Algorithm - Programmed instructions that guide how a system processes data and makes decisions.
Application Use Tracking - The monitoring of which programs or digital tools an employee uses, how often, and for how long.
Biometric Verification - The use of physical or biological traits, such as facial recognition, fingerprints, or heart rate, to identify individuals or monitor their physical state in real time.
Chilling Effect - A term describing how surveillance discourages open communication or dissent.
Emotional Monitoring - A form of AI that claims to detect emotional or cognitive states by analyzing facial expressions, vocal tone, or biometric signals.
EU AI Act - A European Union law set to take effect in 2026 that classifies workplace AI as “high-risk.” It prohibits practices like emotion recognition at work and requires transparency, oversight, and accountability in how AI systems are used.
Keystroke Logging - The practice of recording every key an employee types to track activity levels and assess productivity.

Sources

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Redefining Productivity in the Age of Workplace Surveillance

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