(Un)Fairness in Mobility: Uber, Cabify & Bolt
Ride-hailing platforms engage in price-fixing, labor exploitation and price discrimination through opaque algorithms. Stronger oversight is required.
5/8
trips show price-fixing on key routes
40%
of tips are kept by Uber
0.37
correlation between price and income, with higher fares in low-income areas
In collaboration with:
Observatorio TAS and Taxi Project
The proliferation of artificial intelligence and algorithmic systems in society makes it important to understand their impact on communities.
This audit specifically examines ride-hailing platforms in Spain, which have stretched the boundaries of existing regulations.
The audit, conducted by Eticas, Observatorio TAS and the Taxi Project, aims to identify potential harms caused by the algorithms used by these platforms in terms of competition, labor compliance, and geographic discrimination in consumer pricing.
This report seeks to identify how algorithms and AI systems challenge traditional notions of compliance with competition, labor and consumer law, and the extent to which current legal frameworks sufficiently address these new challenges. In particular, our audit found that these platforms appear to collude in some of the most important routes in Andalusia and Madrid, suggesting indirect price-fixing by algorithmic means in breach of the Law for the Defense of Competition.
The report has also revealed that Uber’s pricing algorithm can discriminate based on the socio-economic characteristics of neighborhoods
Additionally, the use of algorithms in these platforms to mediate labor relations lacks transparency in payment and profiling, leading to discrimination against platform workers for absences due to legally protected reasons. The report has also revealed that Uber’s pricing algorithm can discriminate based on the socio-economic characteristics of neighborhoods, making mobility services less accessible in low-income neighborhoods, which may constitute an infringement of the General Consumer and User Protection Act.
01
The report findings suggest that the pricing algorithms of Uber, Cabify, and Bolt are colluding in some of the most important routes in Andalusia and Madrid, breaching Law 15/2007 for the Defense of Competition (LDC) in Spain. This harms other actors in the market, such as traditional taxis and potential new entrants.
02
Spain’s Law 12/2021 aims to ensure labor rights in the face of challenges posed by the gig economy and ride-hailing platforms. However, the report explores how the rise of algorithmic punishment and opaque algorithms can harm workers. Moreover, algorithms can sanction drivers too, which can limit drivers’ future earning potential and job assignments.
Additionally, ride-hailing apps in Spain lack transparency in their payment structures, particularly in the case of performance incentives and tips. Uber and Cabify drivers report difficulties receiving tips through the app or receiving information regarding when and what proportion of tips is paid out to workers.
03
Ride-hailing apps like Uber, Cabify, and Bolt use surge pricing algorithms to determine ride fares based on supply and demand in a given area and time, resulting in geographic price discrimination. There are indications that prices in ride-hailing apps tend to be lower in more affluent neighborhoods.
This raises concerns about algorithmic price discrimination on the basis of the geographic location and socio-economic makeup of neighborhoods, making mobility services less accessible to disadvantaged groups.
04
The CNMC to investigate the issue of indirect price-fixing by ride-hailing platforms based on the evidence of this report and further inquiry.
VTC drivers to be included in the employment provision of Ley Rider and recognized as employees, rather than contractor workers. More mechanisms for enforcement of Ley Rider’s provision for algorithmic transparency: disclosure of algorithmic processes, and transparent worker profiling, performance assessment and payment structures. Authorities to explore the issue of differences in access conditions in the services of ride-hailing platforms based on geographical location and socio-economic characteristics.
