ACM Home Page
Please provide us with feedback. Feedback
Balancing efficiency and interpretability in an interactive statistical assistant
Full text PdfPdf (488 KB)
Source International Conference on Intelligent User Interfaces archive
Proceedings of the 8th international conference on Intelligent user interfaces table of contents
Miami, Florida, USA
SESSION: Full Technical Papers table of contents
Pages: 181 - 188  
Year of Publication: 2003
ISBN:1-58113-586-6
Authors
Robert St. Amant  North Carolina State University, Raleigh, NC
Michael D. Dinardo  North Carolina State University, Raleigh, NC
Nickie Buckner  North Carolina State University, Raleigh, NC
Sponsors
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 2,   Downloads (12 Months): 15,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues   peer to peer  

Tools and Actions: Review this Article  
Save this Article to a Binder    Display Formats: BibTex  EndNote ACM Ref   
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/604045.604074
What is a DOI?

ABSTRACT

Making an interface more efficient, in a task analysis sense, can make it more difficult for an automated reasoning system to infer user goals, by eliminating some user actions, by presenting information without requiring overt user selection, and so forth. We call the extent to which a system can make such inferences interpretability. In this paper we describe the tradeoff between interpretability and efficiency. We give some general heuristics for improving interpretability in a system and explain how they apply in an implemented system, an assistant for exploratory statistical analysis. Increased interpretability in the system is provided by navigation techniques for data exploration and a data mountain for organizing results; a formative evaluation illustrates some of the potential benefits of applying interpretability heuristics to an intelligent user interface


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
2
 
3
 
4
H. Lieberman. Letizia: An agent that assists web browsing. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 924--929, 1995.
5
 
6
7
8
9
10
 
11
R. St. Amant and P. R. Cohen. Intelligent support for exploratory data analysis. Journal of Computational and Graphical Statistics, 7(4):545--558, 1998.

Collaborative Colleagues:
Robert St. Amant: colleagues
Michael D. Dinardo: colleagues
Nickie Buckner: colleagues

Peer to Peer - Readers of this Article have also read: