Uber deployed a new algorithmic pay structure called 'Upfront Fares' in 24 U.S. cities that replaced transparent time-and-distance calculations with opaque algorithmic pricing, resulting in reduced driver earnings and decreased pay transparency.
Uber Technologies Inc tested a new driver earnings algorithm called 'Upfront Fares' in 24 U.S. cities across Texas, Florida, and the Midwest starting around six months prior to February 2022. The system replaced Uber's traditional pay calculation based on time and distance with an opaque algorithm using unspecified factors including base fares, estimated trip length and duration, real-time demand at destination, and surge pricing. While drivers gained upfront visibility of fare amounts and destinations before accepting rides, the company reduced earnings for longer trips to prevent drivers from avoiding short rides. Driver Sam Vance from Columbus, Ohio reported that trips that previously earned around $1 per mile now paid significantly less, with examples showing Uber taking up to 50% of customer fares compared to their stated average of 25%. The algorithm appears arbitrary to drivers who can no longer calculate expected earnings, with some reporting overall decreased earnings despite Uber's claims of no earnings impact. The changes affected thousands of drivers across the pilot cities, with expansion dependent on driver attraction and retention metrics.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Challenges in understanding or explaining the decision-making processes of AI systems, which can lead to mistrust, difficulty in enforcing compliance standards or holding relevant actors accountable for harms, and the inability to identify and correct errors.
AI system
Due to a decision or action made by an AI system
Intentional
Due to an expected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed