Power to the People: The Role of Humans in Interactive Machine Learning Public Deposited

http://ir.library.oregonstate.edu/concern/articles/rn3013269

This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the American Association for Artificial Intelligence and can be found at:  http://www.aaai.org/Magazine/magazine.php.

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  • Systems that can learn interactively from their end-users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that demonstrate how interactivity results in a tight coupling between the system and the user, exemplify ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. After giving a glimpse of the progress that has been made thus far, we discuss some of the challenges we face in moving the field forward.
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  • Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to the people: The role of humans in interactive machine learning. AI Magazine, 35(4), 105-120.
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  • description.provenance : Approved for entry into archive by Erin Clark(erin.clark@oregonstate.edu) on 2015-06-22T14:50:30Z (GMT) No. of bitstreams: 2 kuleszat1452020263.zip: 7297586 bytes, checksum: 2dfa85b7259edc2fec5791b3e2e9137c (MD5) AIMagazine2015-IML.pdf: 7736867 bytes, checksum: a4138df2fc3d8862f9525b8185c28ab7 (MD5)
  • description.provenance : Made available in DSpace on 2015-06-22T14:50:31Z (GMT). No. of bitstreams: 2 kuleszat1452020263.zip: 7297586 bytes, checksum: 2dfa85b7259edc2fec5791b3e2e9137c (MD5) AIMagazine2015-IML.pdf: 7736867 bytes, checksum: a4138df2fc3d8862f9525b8185c28ab7 (MD5) Previous issue date: 2014
  • description.provenance : Submitted by Open Access (openaccess@library.oregonstate.edu) on 2015-06-19T22:16:17Z No. of bitstreams: 1 AIMagazine2015-IML.pdf: 7736867 bytes, checksum: a4138df2fc3d8862f9525b8185c28ab7 (MD5)

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