AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

Font Size: 
On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems
Besmira Nushi, Ece Kamar, Eric Horvitz, Donald Kossmann

Last modified: 2017-02-12

Abstract


We study the problem of troubleshooting machine learning systems that rely on analytical pipelines of distinct components. Understanding and fixing errors that arise in such integrative systems is difficult as failures can occur at multiple points in the execution workflow. Moreover, errors can propagate, become amplified or be suppressed, making blame assignment difficult. We propose a human-in-the-loop methodology which leverages human intellect for troubleshooting system failures. The approach simulates potential component fixes through human computation tasks and measures the expected improvements in the holistic behavior of the system. The method provides guidance to designers about how they can best improve the system. We demonstrate the effectiveness of the approach on an automated image captioning system that has been pressed into real-world use.

Keywords


troubleshooting; machine learning; crowdsourcing

Full Text: PDF