Social Implications of AI: The Case of Ubers and Ambulances

Artificial intelligence (AI) will reduce use of centralized services and give further rise to decentralized, distributed services. The social implications of this are just beginning to materialize. Consider the case of Uber. Recent academic research found Uber’s entry into new markets decreased ambulance rates in those markets by at least seven percent. In a world with only ambulances, individuals have few options when seeking medical attention. But not all medical needs are life threatening or require an ambulance. Calling an ambulance is a blunt instrument in a world with a wide spectrum of medical needs. The presence of ridesharing services offer an alternative that individuals can use in place of ambulances. The resulting decline in ambulance demand decreases wait times, improves patient survival rates and leads to broad societal welfare improvement. Moreover, when individuals can self-select less expensive alternatives to ambulances or specify which hospital to present at, it should free up scarce, expensive resources and enable these services to be used more efficiently.

The case of Ubers and ambulances is just a single example of things to come as more services are born from AI. Machine learning and other AI techniques are the underlying foundation for every part of ridesharing services and other companies like them. From figuring out the appropriate fare to providing estimated arrival times to ensuring vehicles are positioned in the right quadrants of cities. As self-driving vehicles become prevalent, AI will play a more active role in every aspect of ridesharing services and the services that develop around them.

AI is built on data and AI techniques like machine learning take into account a wide range of digitized information. Consider Uber’s use of machine learning to estimate how long a given Uber Eats delivery will take. Their models will naturally take into account drive times and traffic conditions for different parts of the city at different times of the day. But as both data and sophistication grow, these models will also recognize and take into account that the time needed to make noodles differs from that needed to make a hamburger.

Centralized services are often built on the economic premise of economies of scale. We can lower the cost of rendering services by scaling a service across a wider number of individuals because of the inverse relationship that exists between the per-unit fixed costs of production and the quantity produced. Economies of scale are also driven by a matching problem that exists in the analog world – how to get the right things to the right places for the right people at the right time. Digitization and datafication helps solve this inherent matching problem.

We are already seeing how machine learning techniques are lowering points of friction across a very wide service market. In the case of ridesharing, this might entail telling an individual to walk 15 yards to a nearby street corner to improve accuracy and reduce pick-up time. It would be impossible for humans to optimize this outcome for every street corner in every city of the world.

Cities like Washington, DC are exploring how to integrate ridesharing services or other alternative services into their city-wide EMS systems to lower costs and improve outcomes. Doing so would enable cities to broaden and expand the distribution of available services, and as a result they also would indirectly incorporate AI into their city-wide systems. In the future, 911 systems might include AI nurses who assess the situation in real-time and decide the appropriate response.

AI makes decentralized, distributed services more efficient and effective and in turn will make these services more applicable and important moving forward.

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