Algorithmic control: How digital surveillance is shaping online platform work in Europe

Algorithmic management refers to the use of software algorithms to automate managerial functions traditionally performed by humans. In online platform work, these algorithms execute three key organisational control mechanisms that define the performance of the work. Direction determines what tasks need to be done, in what sequence and within what time frame. Algorithms automatically match projects to workers based on skills, availability or bidding behaviour, and often set time limits for task completion, while guiding the order and manner in which tasks are performed. Evaluation monitors and assesses worker activities and performance. This operates primarily through reputational systems that rate workers based on client-generated feedback, completion rates, response times and other performance metrics. Some platforms supplement client ratings with automated monitoring of worker activity, tracking time spent on tasks, keystrokes, screen activity and communication patterns. Discipline enforces compliance through performance-based sanctions. These range from restricting access to work opportunities or higher-paying assignments, through issuing warnings about potential account suspension, to automatic deactivation of worker accounts for falling below performance thresholds.

The data gathered through the Eurofound–ELA survey, which was conducted across 15 Member States and received almost 4,000 responses, reveal that digital surveillance permeates online platform work across all demographic groups (Table 1). Approximately 78% of respondents experience time-tracking systems that log hours worked and time spent on specific tasks. Communications surveillance, i.e. monitoring emails, messages and platform interactions, affects 67% of workers. Screen monitoring through screenshots and keystroke logging captures the work processes of 53% of respondents.

There is minimal variation by gender or education, according to the data: both men and women, and workers at all educational levels, face similar rates of digital oversight. However, a striking age gradient emerges. Workers aged 50–65 experience substantially lower surveillance rates across all three control mechanisms: 68% face time tracking, compared with 80% of younger workers; 38% experience screen monitoring, compared with 57% among those aged 18–34; and 52% report communications surveillance, compared with 72% of younger workers. This suggests that older workers may self-select into less intensively managed platform arrangements, potentially making the most of their experience and established reputations to access better working conditions.

Beyond direct surveillance, platforms employ sophisticated gamification mechanisms to manage worker behaviour through competitive dynamics. The survey reveals that performance rankings and points systems are nearly as pervasive as surveillance itself.

Leaderboards displaying workers' relative standing affect 64–70% of online platform workers, while points or ratings systems that quantify worker quality and reliability impact 71–76% of respondents. These systems transform work into a perpetual tournament, in which workers don't just complete tasks but also compete against each other for rankings that determine future opportunities, access to higher-paying work and even continued access to the platform.

As with surveillance, age constitutes the primary differentiating factor. Workers aged 50–65 face gamification at substantially lower rates: only 49% experience leaderboards, compared with 70% among younger age groups, and 58% face points systems, compared with 76% among workers under 35. This difference of 20–27 percentage points mirrors the monitoring gradient, suggesting that older workers systematically concentrate on platforms employing less-intensive algorithmic management systems.

The final dimension of algorithmic management concerns how platforms enforce compliance through sanctions. The survey reveals a three-tier disciplinary system that distributes relatively evenly across the online platform workforce.

Approximately one-third of respondents face no performance-based sanctions, suggesting that these workers provide services for platforms with more lenient or graduated approaches to performance management. However, more than 40% of workers operate under warning systems, where delivering tasks that are assessed as substandard triggers alerts about potential consequences before more severe actions are taken. Furthermore, a substantial minority of online platform workers (20–27%) report providing services through platforms that implement immediate termination or account suspension policies for services that fall below performance thresholds.

Again, age demonstrates the strongest gradient, with older workers less likely than those in younger age groups to experience sanctions. Interestingly, however, the data also indicate that highly educated workers face immediate termination at higher rates than workers with low educational attainment. This finding is driven by the self-selection of highly educated workers into task categories with more stringent performance requirements – for instance, software development or technology consulting, where technical errors potentially carry greater consequences.

The various dimensions of algorithmic management don't operate independently. Rather, they combine into distinct patterns that affect the working conditions of online platform workers. Using the data gathered through the survey Eurofound has identified four distinct algorithmic management regimes operating simultaneously within online platform work.

• Comprehensive control – affecting 43% of respondents this is the most intensive algorithmic management regime, combining high surveillance, full gamification systems, restricted task autonomy through automatic or client-driven assignment, and performance-based disciplinary enforcement. Workers subject to this regime experience the full weight of algorithmic management across all dimensions.

• A gamified assignment regime – this affects almost one-third of the surveyed workforce. While direct monitoring is moderate, this regime combines strong gamification pressure with restricted worker autonomy over task selection. Platforms in this regime exercise control primarily through competitive dynamics and assignment mechanisms, rather than intensive surveillance. Workers still face significant algorithmic control, but the regime operates more through competitive ratings and restricted access to tasks than through continuous monitoring of work processes.

• A freelance surveillance regime – 14% of surveyed workers are affected by this. Platforms using this type of algorithmic management practice allow workers autonomy over task selection but subject them to intensive monitoring once tasks are undertaken. Time tracking, screen surveillance and communications monitoring remain pervasive in this regime, but workers retain more control over what work they do and when they do it.

• Low control ‒ another 14% of workers provide services on platforms that use low-control algorithmic management practices, characterised by minimal monitoring, limited gamification, high autonomy in task selection and low disciplinary intervention. The experience of workers in this regime is most similar to that of providers of traditional freelance professional services, with platforms serving primarily as marketplaces rather than comprehensive management systems.

Looking at algorithmic management regimes by task type, the survey reveals that workers performing the most cognitively demanding and highly skilled tasks face the most intensive algorithmic management (Figure 1).

More than half of software developers, AI services workers and technology consultants operate under comprehensive control regimes. Technology consultants show a similar pattern (51%). Conversely, technical workers report the lowest access to high-autonomy arrangements: just 10% of software developers, 9% of technology consultants and 5% of AI services workers operate under low-control regimes.

These findings challenge conventional wisdom about algorithmic management in platform work. Although the literature on digital Taylorism (the splitting of complex work processes into simpler, smaller tasks) suggests that algorithmic control works best for routine, standardised tasks that can be easily monitored and evaluated, the survey reveals that online labour platforms successfully deploy comprehensive algorithmic management even for highly complex professional work. The regime likely involves the ‘unbundling’ of professional services into specific tasks that can be subjected to strict management by algorithms.

The findings from the Eurofound–ELA survey confirm the widespread use of intrusive algorithmic management practices in online platform work. With three-quarters of workers experiencing constant time tracking and nearly half operating under comprehensive control regimes, it does appear that professional work carried out through online labour platforms is a test case for the use of algorithmic management systems in traditional employment contexts, where they are increasingly being deployed. They also provide support for the approach taken in the Platform Work Directive, which grants certain rights to all platform workers regardless of whether they're classified as employees or independent contractors. The provisions of the directive directly address the most concerning practices: prohibiting surveillance of private conversations, requiring platforms to explain how their automated systems work and make decisions, and guaranteeing workers the right to human review and appeal of automated decisions that affect their accounts, payments or working conditions.