Zillow’s artificial intelligence failure and its impact on perceived trust in information systems

Abstract

With increasing machine learning and artificial intelligence adoption in organizations, consumers are frequently exposed to recommendations from various algorithms. In 2007, Zillow, one of the largest online real estate firms, invested in technology to transform into Zillow 2.0 by leveraging its iBuying machine learning algorithm to purchase homes directly from consumers. Although it was initially successful, the company eventually incurred significant losses due to inaccurate future price predictions resulting in the closure of iBuyer business in November 2021. This case underscores the complexity of algorithmic failures and the need for caution and thorough understanding in the face of the unpredictability and risk of advancing technology. It also highlights the pivotal role of perceived consumer trust when using information systems, a responsibility that should not be taken lightly. It accentuates the weight of responsibility that comes with leadership in ethical considerations, thereby enhancing the decision-making process when utilizing machine learning algorithms and in the context of artificial intelligence failures.