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Awareness, training and trust in interaction with adaptive spam filters
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Conference on Human Factors in Computing Systems archive
Proceedings of the 27th international conference on Human factors in computing systems table of contents
Boston, MA, USA
SESSION: Security table of contents
Pages 909-912  
Year of Publication: 2009
ISBN:978-1-60558-246-7
Authors
Henriette S.M. Cramer  University of Amsterdam, Amsterdam, Netherlands
Vanessa Evers  University of Amsterdam, Amsterdam, Netherlands
Maarten W. van Someren  University of Amsterdam, Amsterdam, Netherlands
Bob J. Wielinga  University of Amsterdam, Amsterdam, Netherlands
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Even though adaptive (trainable) spam filters are a common example of systems that make (semi-)autonomous decisions on behalf of the user, trust in these filters has been underexplored. This paper reports a study of usage of spam filters in the daily workplace and user behaviour in training these filters (N=43). User observation, interview and survey techniques were applied to investigate attitudes towards two types of filters: a user-adaptive (trainable) and a rule-based filter. While many of our participants invested extensive effort in training their filters, training did not influence filter trust. Instead, the findings indicate that users' filter awareness and understanding seriously impacts attitudes and behaviour. Specific examples of difficulties related to awareness of filter activity and adaptivity are described showing concerns relevant to all adaptive and (semi-)autonomous systems that rely on explicit user feedback.


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.

 
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Collaborative Colleagues:
Henriette S.M. Cramer: colleagues
Vanessa Evers: colleagues
Maarten W. van Someren: colleagues
Bob J. Wielinga: colleagues