Error rates in the assessment of routine claims for welfare benefits have been found to be very high in Netherlands, USA and UK. This is a significant problem both in terms of quality of service and financial loss through over payments. These errors also present challenges for machine learning programs using the data. In this paper we propose a way of addressing this problem by using a process of moderation, in which agents argue about the classification on the basis of data from distinct groups of assessors. Our agents employ an argument based dialogue protocol (PADUA) in which the agents produce arguments directly from a database of cases, with each agent having their own separate database. We describe the protocol and report encouraging results from a series of experiments comparing PADUA with other classifiers, and assessing the effectiveness of the moderation process.
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