Algorithmic Management in the Gig Economy: Balancing Efficiency and Equity
The gig economy has transformed the European labour market, offering millions of workers flexibility and autonomy that traditional employment cannot match. Yet, this transformation has also ushered in a new era of algorithmic management, where opaque digital systems allocate tasks, monitor performance and even determine pay. For many gig workers, the algorithm has become the “invisible boss” – efficient, but often inscrutable and unaccountable.
The European Union’s Platform Work Directive, adopted in 2024, is a pioneering legislative response to these challenges. The Directive mandates unprecedented transparency for digital labour platforms, compelling them to disclose the criteria and variables that influence algorithmic decisions. These include how tasks are distributed, how pay is calculated and how performance ratings are assigned. The aim is to empower workers with the information they need to understand and contest automated decisions, countering the power imbalance that has long characterised platform work.
Research published by Eurofound earlier this year paints a stark picture: in Paris, 87% of food delivery couriers receive work instructions primarily through algorithms, yet the majority cannot explain how these decisions are made. This opacity has real-world consequences. In Lisbon, labour courts reported a 214% increase in “algorithmic grievance” cases, as workers challenge decisions they perceive as arbitrary or unfair. The Directive’s transparency provisions are designed to address these conflicts by fostering accountability and human oversight.
Spain’s 2021 “Ley Rider” offers a compelling case study in the practicalities of algorithmic management reform. The law requires delivery platforms to share real-time data on surge pricing, provide advance notice of changes to rating systems and establish worker-led audit committees. The results have been mixed but instructive: rider satisfaction improved significantly, but platforms like Glovo reported profit declines, highlighting the delicate balance between ethical management and business viability.
The Directive goes further than national laws, extending protections across the EU and introducing new rights for gig workers. These include the right to seek human intervention in automated decisions, the right to challenge and review those decisions and limitations on the processing of personal data for algorithmic management purposes. The provisions are grounded in the General Data Protection Regulation (GDPR), but are tailored to the unique challenges of platform work, focusing on accountability, transparency, explainability and the prevention of bias and opacity.
Beyond compliance, some platforms are embracing transparency as a competitive advantage. Copenhagen’s Hilfr, Danish home-cleaning platform, involved workers in algorithm training, resulting in a 29% increase in retention rates. Such initiatives demonstrate that ethical algorithmic management can enhance trust, reduce turnover and support long-term sustainability.
However, challenges remain. The Directive’s effectiveness depends on robust enforcement and ongoing adaptation to technological change. Critics argue that some provisions remain ambiguous, risking loopholes that could undermine worker protections. There is also the question of scalability: while large platforms may have the resources to comply, smaller firms may struggle with the costs of transparency and audit requirements.
In conclusion, algorithmic management in the gig economy presents both opportunities and risks. The EU’s regulatory framework sets a high bar for transparency and fairness, challenging platforms to rethink their operational models. For workers, these changes promise greater agency and protection; for platforms, they signal a shift towards more responsible, human-centred innovation. The coming years will test whether these reforms can deliver on their promise, but the direction of travel is clear: the era of the invisible, unaccountable algorithm is drawing to a close.