Airbnb uses machine learning to help hosts optimize their pricing, which generates more revenue for both hosts and Airbnb itself.
This presentation by Amber Cartwright describes the design process Airbnb went through to deliver a successful system. Two takeaways to note here:
Decision support rather than automated decision making
From the presentation it is clear that a thoughtful process went into designing a price optimization system. Notice that system does not use machine learning to fully automate the decision making process but rather is a decision support system that encourages hosts to make better pricing decisions while decreasing the time and effort required to achieve those results.
Initially Airbnb created a switch for their hosts that allowed the algorithm to automatically set prices for hosts’ units. They found that hosts were uncomfortable with giving up full control. Therefore, the team modified the design to add guardrails — minimum rent allowed and maximum rent allowed. They also added a setting that allowed hosts to set the general frequency of rentals (essentially low, medium, high but in more host-friendly language).
It is natural for data scientists to gravitate towards automated decision making, it reduces the number of variables and emphasises their contribution. However, as machine learning disseminates through our enterprises we can expect to see a large percent of those projects being decision support systems, which will require different methods and tools from pure automated decision making. This will be particularly true as we apply machine learning to more consequential decisions.
Explainability is key to deliver system utility
Note that the machine learning model is just one part of the overall system. The UX design, the traditional software the majority of the system, and the human judgements of the hosts are all equally important parts of the overall system.
The machine learning algorithm applied by the team is not the most sophisticated available. Which means looking at the algo in isolation it might appear to underperform relative to alternatives. However, one benefit of the chosen algo is straightforward explainability, which enabled a successful UX design which in turn powered adoption by the hosts and integration in the decision making of info that is available to hosts but is not available to the algo. Having a more powerful algo that leads to an inscrutable UX and low adoption would deliver model metrics but not enterprise utility.