Confirmation Bias in the Analysis of Remote Sensing Data

Analysis of remote sensing data requires a mix of technical data analysis and expert judgment. Although there is considerable empirical evidence that expert judgments reflect substantial biases, the impact of judgment biases in remote sensing and similar types of technical data analyses has not been...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2009-01, Vol.39 (1), p.218-226
Main Authors: Lehner, P.E., Adelman, L., DiStasio, R.J., Erie, M.C., Mittel, J.S., Olson, S.L.
Format: Article
Language:English
Subjects:
Acoustic sensors
Alternative causes approach
Anti-freeze
Bias
Chemicals
confirmation bias
Cybernetics
Data analysis
Data processing
Empirical analysis
Floods
heuristics and biases
Human
Judgments
Leaching
Magnetic sensors
Remote sensing
Sensors
Sonar detection
Studies
technical data analysis
Thermal force
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Summary:Analysis of remote sensing data requires a mix of technical data analysis and expert judgment. Although there is considerable empirical evidence that expert judgments reflect substantial biases, the impact of judgment biases in remote sensing and similar types of technical data analyses has not been investigated. In particular, judgment research suggests that experts are prone to a confirmation bias-where focus on a proposed hypothesis leads the expert to seek and overweigh confirming versus disconfirming evidence. In technical data analysis, this predicts a tendency toward false positives in interpretation-concluding that sensor data support a hypothesis when they do not. In this paper, we empirically examine confirmation bias in technical data analysis of remote sensing data, along with an approach to mitigating this bias that systematically promotes consideration of alternative causes in the analysis. Results suggest that analysts do exhibit confirmation bias in their technical data analysis of remote sensing data, and furthermore, that structured consideration of alternative causes mitigates this bias.
ISSN:1083-4427
2168-2216
1558-2426
2168-2232
DOI:10.1109/TSMCA.2008.2006372