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Register a new userGenerating User-friendly Explanations for Loan Denials using GANs
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Added by
Ramya Srinivasan
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI decisions to various stakeholders. State-of-the-art explainable AI systems mostly serve AI engineers and offer little to no value to business decision makers, customers, and other stakeholders. Towards addressing this gap, in this work we consider the scenario of explaining loan denials. We build the first-of-its-kind dataset that is representative of loan-applicant friendly explanations. We design a novel Generative Adversarial Network (GAN) that can accommodate smaller datasets, to generate user-friendly textual explanations. We demonstrate how our system can also generate explanations serving different purposes: those that help educate the loan applicants, or help them take appropriate action towards a future approval.
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Most explainable AI (XAI) techniques are concerned with the design of algorithms to explain the AIs decision. However, the data that is used to train these algorithms may contain features that are often incomprehensible to an end-user even with the best XAI algorithms. Thus, the problem of explainability has to be addressed starting right from the data creation step. In this paper, we studied this problem considering the use-case of explaining loan denials to end-users as opposed to AI engineers or domain experts. Motivated by the lack of datasets that are representative of user-friendly explanations, we build the first-of-its-kind dataset that is representative of user-friendly explanations for loan denials. The paper shares some of the insights gained in curating the dataset. First, existing datasets seldom contain features that end users consider as acceptable in understanding a models decision. Second, understanding of the explanations context such as the human-in-the-loop seeking the explanation, and the purpose for which an explanation is sought, aids in the creation of user-friendly datasets. Thus, our dataset, which we call Xnet, also contains explanations that serve different purposes: those that educate the loan applicants, and help them take appropriate action towards a future approval. We hope this work will trigger the creation of new user friendly datasets, and serve as a guide for the curation of such datasets.
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