Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/250385 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
ZEW Discussion Papers No. 22-001
Verlag: 
ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung, Mannheim
Zusammenfassung: 
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.
Schlagwörter: 
machine learning
accountability gap
computer vision
real estate
urban studies
JEL: 
C52
R30
Dokumentart: 
Working Paper

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