Bias and Representativeness in Linked Data

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Data are collected and generated at increasingly fast rates by companies and organizations in various domains. Data describing a single entity (e.g., person, product) can be collected by different organizations or by different units within the same organization. Not only that data are collected by different organizations, but it may be collected in different ways and at different granularity (e.g., one company may collect city for location of a customer, while another may collect province). Nowadays it is well accepted that by integrating data from different sources, we enrich the knowledge about entities of interest, thus making the data more valuable for analysis and prediction. Although data integration is a challenging problem, recent technological advances have made it possible to create large data lakes where data are unified from multiple sources. However, we are far away from integrating data fully (i.e., finding all the entities across the different data sources). This is due to the fact that unique identifiers across data collections do not exist, thus one must use common characteristics to all of the databases and compare their values to determine similarity. In addition, other challenges are presented by different database schemas, typographical errors and missing data. What is the impact of false positives (mismatches) and false negatives (missed matches) when data integrated from multiple sources are used for analysis or as training data for artificial intelligence -based methods? The main objective of the proposed research program is to investigate, understand and mitigate the biased data that are created through data integration. The understanding of the bias, representativeness and quality of data is critical. This is especially true when data are used in circumstances that could affect society at large (e.g., healthcare, policy making). Towards achieving the goal of the proposed program, the research plan is divided between a set of short- term goals and a set of long-term goals. The set of short -term goals are as follows: (1) investigating the state- of -the -art systems currently employed in data integration and record linkage; (2) developing a comparison framework and proposing new methods to investigate representativeness and bias in data generated by unifying multiple data sources; and (3) testing and evaluating the techniques and comparison methods in diverse applications (e.g., healthcare, retail) with different types of entities (e.g., persons, products). The long-term goal involves developing new methods and new technologies to mitigate the bias and to improve the quality and value of the data generated through data integration techniques.