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Consequently, there has been a recent surge in interpreting decisions of such complex models in order to explain their actions to humans. Models which correspond to human interpretation of a task are more desirable in certain contexts and can help attribute liability, build trust, expose biases and in turn build better models. It is therefore crucial to understand how and which models conform to human understanding of tasks. In this paper we present a large-scale crowdsourcing study that reveals and quantifies the dissonance between human and machine understanding, through the lens of an image classification task. In particular, we seek to answer the following questions: Which (well performing) complex ML models are closer to humans in their use of features to make accurate predictions? How does task difficulty affect the feature selection capability of machines in comparison to humans? Are humans consistently better at selecting features that make image recognition more accurate? Our findings have important implications on human-machine collaboration, considering that a long term goal in the field of artificial intelligence is to make machines capable of learning and reasoning like humans.<\/jats:p>","DOI":"10.1145\/3359158","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T13:40:21Z","timestamp":1573220421000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":50,"title":["Dissonance Between Human and Machine Understanding"],"prefix":"10.1145","volume":"3","author":[{"given":"Zijian","family":"Zhang","sequence":"first","affiliation":[{"name":"Leibniz University of Hannover, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaspreet","family":"Singh","sequence":"additional","affiliation":[{"name":"Leibniz University of Hannover, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ujwal","family":"Gadiraju","sequence":"additional","affiliation":[{"name":"Leibniz University of Hannover, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Avishek","family":"Anand","sequence":"additional","affiliation":[{"name":"Leibniz Universit\u00e4t Hannover, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3174156"},{"key":"e_1_2_1_2_1","volume-title":"Daniel LK Yamins, and James J DiCarlo","author":"Afraz Arash","year":"2014","unstructured":"Arash Afraz , Daniel LK Yamins, and James J DiCarlo . 2014 . Neural mechanisms underlying visual object recognition. In Cold Spring Harbor symposia on quantitative biology, Vol. 79 . Cold Spring Harbor Laboratory Press , 99--107. Arash Afraz, Daniel LK Yamins, and James J DiCarlo. 2014. Neural mechanisms underlying visual object recognition. In Cold Spring Harbor symposia on quantitative biology, Vol. 79. Cold Spring Harbor Laboratory Press, 99--107."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the HCOMP 2018 Works in Progress and Demonstration Papers Track of the sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2018","author":"Anand Avishek","year":"2018","unstructured":"Avishek Anand , Kilian Bizer , Alexander Erlei , Ujwal Gadiraju , Christian Heinze , Lukas Meub , Wolfgang Nejdl , and Bjoern Steinroetter . 2018 . Effects of Algorithmic Decision-Making and Interpretability on Human Behavior: Experiments using Crowdsourcing . 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