IML4DQ: interactive machine learning for data quality with applications in credit risk

Abstract

Data Quality (DQ) has gained popularity in recent years due to the increasing reliance on data in machine learning (ML). The DQ domain itself can benefit from ML, which is able to learn from large amounts of data, saving time and resources required by manual DQ assurance. To extend the accessibility of ML solutions and incorporate human input, Interactive ML (IML) integrates ML with a user interface (UI) that facilitates a human-in-the-loop approach. Both high-quality data and human involvement are critical in credit risk management (CRM), where poor DQ can lead to incorrect decisions, causing both ethical issues and financial losses. This paper introduces IML4DQ, a novel IML-based solution designed to ensure DQ in CRM through a dedicated UI. The IML4DQ design is grounded in established IML practices and key UI design principles. A rigorous evaluation using behavioral change theories reveals new insights into the significance of instrumental attitude and government- and management-based norms in shaping attitudes towards DQ in CRM, as well as positive attitude towards automating DQ processes with IML.

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