Proceedings of the 6th Bayesian Modeling Applications Workshop at the 24th Annual Conference on Uncertainty in AI: UAI 2008, Helsinki, Finland (July).
Bias is intrinsic to observation and reasoning, whether done by humans or automated systems. The consumers of data that includes biases must be aware of these biases to correctly use the biased information. Through Charles River’s work on encoding expert knowledge into intelligent systems, automating sensor processing, and data fusion, we have developed a suite of methods for representing biases and integrating these representations into reasoning processes. This paper presented those methods along with discussion of their appropriate use and strengths and weaknesses.
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