3 lessons Zillow can teach us about the problem with making decisions based on algorithms, by tony_m_evans
These algorithms are often as flawed as the humans who create them.Two years ago, the real-estate company Zillow began a program to buy and sell homes. The idea of the program, Zillow Offers, was to use the company’s large amount of historical real-estate data to build extremely complex predictive models capable of quickly estimating the market value of homes. After filling out a short form, sellers received an offer within two business days.
The story of Zillow Offers is a reminder that algorithms are as fallible as the humans who create them. As algorithms play an increasingly important role in business and everyday life, it’s becoming more important to understand when they are likely to fail.An algorithm can only make accurate predictions if you train it using the right type of data. For example, the characteristics that make a house valuable in San Francisco might not matter as much in other parts of the country .
The issue of training is particularly important when models are trying to predict outcomes involving people. In one, Amazon built an algorithm to identify top talent in the resumes of job applicants. Amazon’s model was based on finding applicants who matched top employees currently working for the company. Because Amazon already had many successful men in top positions, the algorithm strongly preferred resumes from men and penalized resumes from women.2.
Building a model to focus exclusively on one thing might lead to ignoring other important considerations.The last problem for algorithms is that some outcomes are just harder to predict than others. In particular, studies focusing on human relationships have found that algorithms struggle to predict romanticand long-term relationship outcomes .
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