Food delivery workers in Edinburgh and Scotland are calling on major platforms to increase transparency regarding the algorithms that determine job assignments and pay rates. Workers affiliated with the Workers’ Observatory, a charity established by gig economy workers in collaboration with academics from St Andrews and Edinburgh universities, report that their earnings have declined substantially despite maintaining consistent work hours and delivery volumes.
One rider with seven years of experience stated his income has fallen to half of what he earned four years ago. Another worker noted that in the past three years, the reduction in available work has made it impossible to cover basic living expenses through part-time delivery work. A third rider tracked his own earnings data, documenting a steady decline in per-order fees from £3.67 in 2023 to £3.42 in the first half of 2026, despite maintaining consistent delivery rates of 3.6 to 3.8 orders per hour.
Workers attribute the pay reductions to increased platform reliance on dynamic pricing algorithms and other algorithmic practices. The platforms employ machine-learning systems that determine which jobs to offer specific workers and adjust compensation levels in real time. The Workers’ Observatory has conducted research experiments monitoring these variable pay levels and found that the algorithmic systems operate without transparency to workers.
This situation reflects broader concerns about algorithmic decision-making in the gig economy. Trades unions have campaigned to restrict dynamic pricing practices, citing worker uncertainty about earnings. A recent class action lawsuit filed in Amsterdam by drivers from multiple countries alleges that ride-hailing app algorithms breach data protection laws and suppress earnings based on individual driver behavior. The major delivery platforms maintain that their systems optimize efficiency rather than unfairly penalize workers, though workers and researchers argue the decision-making processes remain opaque and difficult to challenge.
Article Attribution | Read More at Article Source
Article summary produced by Claude AI