You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey 
Chapter Title:  D. Biased inspection results 
1. Abstract of Cited Paper (BIB001):  Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.

Literature Review:
In statistics, samples of data must represent the overall population in order to make valid conclusions. This is a longstanding issue in statistics and therefore in machine learning, too, as discussed in  . In the preliminary work of BIB002 we noticed that the sample of previously inspected customers may not be representative of all customers. One reason is, for example, that electricity suppliers previously focused on certain neighborhoods for inspections. NTL classifiers trained on biased inspection data are likely to be biased, too. To the best of our knowledge, this topic has not been addressed in the literature on NTL detection. Bias correction has initially been addressed in the field of computational learning theory BIB001 . We believe that it can be promising to explore in this direction. For example, one promising approach may be resampling inspection data in order to be representative in terms of location and type of customer.