Crowd Discrimination in the Digital Workplace – When the “Wisdom of the Crowd” Turns Against Workers

An Uber driver loses access to the app after passengers give him slightly lower ratings; a job applicant is rejected again and again by automated screening tools that never explain why; a care worker is dismissed overnight after a photo from her private life goes viral and triggers a wave of online outrage.

These stories might look unrelated, but in fact they reveal a shared pattern: employment decisions are increasingly shaped by the judgements of “the crowd” – customers, users, online communities and decision-makers – as channelled through platforms, rating systems and AI tools. What appears as neutral technology quietly imports social prejudice into the workplace. I refer to this pattern as “crowd discrimination”: discrimination that does not stem from a single biased employer, but from a dense web of reviews, scores, historical data and online reactions. Digital infrastructures collect these signals, process them and translate them into decisions about who receives a job offer, who is allocated work and who is excluded from the labour market altogether. It is a form of discrimination that is technologically mediated, pervasive and difficult to challenge through traditional legal frameworks. In what follows, I look at platform work, AI-driven management and online shaming to show how this dynamic works in practice – and why the law is finding it hard to catch up.

Platform Work

Platform work is the clearest starting point. Ride-hail and delivery apps are built around ratings and reviews. On paper, these scores are simply tools for monitoring service quality. In practice, they perform much more powerful work. They influence who is shown which trips, how much workers earn, and when they are deactivated. Empirical research indicates that customer ratings – the crowd’s aggregated judgements – are not immune from longstanding forms of bias: workers who are women, racialised, older or speak with an accent often receive lower ratings even when their performance is identical. The platform’s algorithm then gives these biased numbers the force of policy. A half-star difference can mean fewer opportunities, less income and, eventually, disconnection from the app.

AI-Driven Management

A similar dynamic appears in AI-driven recruitment and management systems. Many employers now rely on automated tools to decide who sees a job advert, whose CV is shortlisted, how candidates’ video interviews are scored, or which workers are flagged for promotion or dismissal. These tools are trained on data generated by people – past hiring decisions, clicks on adverts, ratings and other traces of everyday behaviour. In other words, they learn from the crowd. If, over many years, the crowd has favoured men from certain schools, neighbourhoods or backgrounds, the system will treat that pattern as a sign of what a “good” worker looks like and quietly downgrade others. No one needs to programme the system to discriminate; it is simply picking up and replaying the crowd’s own biases. For workers, this often just means that applications disappear into a black box and come back as a “no”, with no explanation.

Online Shaming

Online shaming completes the picture. Social media has created a permanent stage on which workers can be judged by the crowd not only for their professional conduct but for almost every aspect of their lives. A short video, a photo taken out of context or a single tweet can trigger a “storm” of crowd reactions that employers experience as a reputational emergency. Dismissal or suspension then becomes a way of signalling distance from the controversy. Yet the content of these storms is often deeply gendered, racialised or otherwise biased. The employer’s decision is formally framed as damage control, but many times it is simply the crowd – with its own mix of prejudices and pressures – that drives the decision.

Connectivity and its Discontents

In all three settings, connectivity is the key. Digital infrastructures make it easy to collect the reactions of large numbers of people, to aggregate them into scores or data patterns and to feed them directly into managerial decisions. Customers, users and online audiences do not sit in HR meetings, but they effectively gain a seat at the table. Their preferences and prejudices become part of the way work is allocated, evaluated and rewarded.

This raises a fundamental challenge for equality law. Traditional anti-discrimination frameworks were designed for a workplace in which the central actor is a recognisable employer – a firm with managers, policies and hierarchies. They focus on intentional discrimination or, more often, on formally neutral rules that disproportionately affect protected groups. Crowd discrimination is different. It is diffuse, technologically mediated and often opaque.

Who, then, is discriminating? The customers who give biased ratings? The platform that turns these ratings into deactivation thresholds? The AI vendor whose model quietly filters out certain candidates? The employer who relies on outputs it barely understands? Each actor can point the finger elsewhere. The employer is “just following the data”, the tech company is “just providing a tool”, the crowd is “just expressing preferences”. Responsibility slips through the gaps between them. Doctrinally, we might try to fit these cases into familiar categories, such as disparate impact or the rejection of customer-biased preferences as a defence. But each category becomes harder to use when the relevant decisions are hidden inside proprietary algorithms or buried in large datasets and social media ecosystems. Workers rarely know which systems were used, what data they relied on or how scores or reactions were translated into decisions. Courts, for their part, often demand detailed technical evidence before they are willing to assign liability. The risk is that discrimination intensifies precisely when it becomes hardest to see.

A Procedure for Better Systems

If we take crowd discrimination seriously, we also need to take procedure seriously. In these settings, workers are typically expected to prove discrimination, yet employers, platforms and vendors control the systems, the data and the explanations. In a digital workplace, that imbalance is not a side issue; it is what makes crowd-based bias so difficult to challenge in practice. One response is to recalibrate the burden of proof. Where a worker can plausibly point to an algorithmic tool, a rating mechanism or a platform rule as playing a material role in the decision, the employer should be required to demonstrate that the process was assessed for discriminatory effects and that safeguards were in place. This should be coupled with concrete procedural rights: timely notice when automated or platform-based tools were used, an intelligible explanation, and access to meaningful human review.  Some interventions can also reduce the risk at source. For example, limiting early-stage exposure to identity cues in recruitment can help to curb background-based bias before candidates reach interview. Finally, workers’ representatives should have a genuine role in the design and deployment of these systems, so that equality concerns are addressed upstream, rather than only after harm has occurred.

Beyond this, platform and AI firms cannot be treated as legally peripheral. They design and supply the tools that increasingly determine who is screened in, ranked down or excluded, and they often control the data needed to detect bias. Yet current frameworks tend to address these actors primarily through obligations owed to the state, rather than by recognising responsibilities that workers can enforce directly against technology providers. Responsibility therefore needs to attach to these firms themselves. One existing doctrinal route, already recognised in limited form by the courts, is an agency-based approach. Where a vendor’s system effectively performs a core employment function, the provider can be brought within the doctrinal frame as an agent rather than treated as a neutral intermediary. This approach should be developed further. And because discrimination is especially difficult to prove when the relevant evidence sits inside proprietary systems, such responsibility must be accompanied by procedural measures: meaningful disclosure, external scrutiny, and a shift in the burden of proof once a claimant can raise a credible indication of disproportionate impact.

Crowd discrimination shows that the future of work is not shaped only in parliaments, courts or collective agreements. It is also being built into rating formulas, data pipelines and AI models. If law and policy do not keep up with these systems, technological innovation will sit alongside a quieter, more fragmented form of discrimination that is harder to spot and challenge.