Towards accountability in machine learning applications: A system-testing approach - Wayne Xinwei Wan; Thies Lindenthal (ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung, 2022)
A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the 'disruption' of traditional processes. However, an accountability gap prevails: How do the models arrive at their predictions? Do they do what we hope they do - or are corners cut? Training ML models is a software development process at heart. We suggest to follow a dedicated software testing framework and to verify that the ML model performs as intended. Illustratively, we augment two ML image classifiers with a system testing procedure based on local interpretable model-agnostic explanation (LIME) techniques. Analyzing the classifications sheds light on some of the factors that determine the behavior of the systems.
Keywords: C52; R30; 330; machine learning; accountability gap; computer vision; real estate; urban studies