Name
Abstract | ||
|---|---|---|
We present an empirical study where the effects of three levels of system transparency of an automated target classification aid on fighter pilots' performance and initial trust in the systemwere evaluated. The levels of transparency consisted of (1) only presenting text-based information regarding the specific object (without any automated support), (2) accompanying the text-based information with an automatically generated object class suggestion and (3) adding the incorporated sensor values with associated (uncertain) historic values in graphical form. The results show that the pilots needed more time to make a classification decision when being provided with display condition 2 and 3 than display condition 1. However, the number of correct classifications and the operators' trust ratings were the highest when using display condition 3. No difference in the pilots' decision confidence was found, yet slightly higher workload was reported when using display condition 3. The questionnaire results report on the pilots' general opinion that an automatic classification aid would help them make better and more confident decisions faster, having trained with the system for a longer period. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-07515-0_3 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Classification support,automation transparency,uncertainty visualization,fighter pilots | Data mining,Transparency (graphic),Workload,Object Class,Artificial intelligence,Operator (computer programming),Engineering,Empirical research,Machine learning | Conference |
Volume | ISSN | Citations |
8532 | 0302-9743 | 6 |
PageRank | References | Authors |
0.49 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tove Helldin | 1 | 76 | 8.59 |
| Ulrika Ohlander | 2 | 8 | 2.42 |
| Göran Falkman | 3 | 173 | 22.13 |
| Maria Riveiro | 4 | 133 | 18.64 |