Explaining Supervised Learning Models: A Preliminary Study on Binary Classifiers

• Xiaomei Wang ^[1] ; Ann M. Bisantz ^[1] ; Matthew L. Bolton ^[1] ; Lora Cavuoto ^[1] ; Varun Chandola ^[1]
  1. [1] University at Buffalo, State University of New York

     University at Buffalo, State University of New York

     City of Buffalo, Estados Unidos

     
• Localización: Ergonomics in Design: The Quaterly of Human Factors Applications, ISSN 1064-8046, Vol. 28, Nº. 3, 2020 (Ejemplar dedicado a: Machine Learning, Artificial Intelligence, and Human Factors Design), págs. 20-26
• Idioma: inglés
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• Resumen

  • The reach of artificial intelligence continues to grow, particularly with the expansion of machine learning techniques that capitalize on increased computing power. Such systems could have tremendous benefits by providing predictions and suggestions. However, they are limited by the fact that they offer incomplete explanations of their predictions to human decision makers. The objective of this work was to summarize general information that could help users make judgments about whether a system is trustworthy and whether the system’s training “makes sense.” A preliminary study was summarized to show the importance of iterative design and testing for visualizing explanations.