Effective End-User Interaction with Machine Learning

Saleema Amershi, James Fogarty, Ashish Kapoor, Desney S. Tan

AAAI 2011 pp. 1529-1532

doi:10.1609/AAAI.V25I1.7964 /aaai/2011/amershi2011aaai-effective/

Abstract

End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate that interactions designed to balance the needs of end-users and machine learning algorithms can significantly improve the effectiveness of end-user interactive machine learning.

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Text

Amershi et al. "Effective End-User Interaction with Machine Learning." AAAI Conference on Artificial Intelligence, 2011. doi:10.1609/AAAI.V25I1.7964

Markdown

[Amershi et al. "Effective End-User Interaction with Machine Learning." AAAI Conference on Artificial Intelligence, 2011.](https://mlanthology.org/aaai/2011/amershi2011aaai-effective/) doi:10.1609/AAAI.V25I1.7964

BibTeX

@inproceedings{amershi2011aaai-effective,
  title     = {{Effective End-User Interaction with Machine Learning}},
  author    = {Amershi, Saleema and Fogarty, James and Kapoor, Ashish and Tan, Desney S.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2011},
  pages     = {1529-1532},
  doi       = {10.1609/AAAI.V25I1.7964},
  url       = {https://mlanthology.org/aaai/2011/amershi2011aaai-effective/}
}