Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

The advancements in machine learning and artificial intelligence can significantly benefit the diagnosis of Non-Communicable Diseases (NCDs). However, the inherent complexity of black-box models hinders the interpretability of the model. Potential regulatory issues arise, and the lack of trust within the medical community is apparent due to the lack of understanding of how and why a model made a prediction. In this study, we demonstrate how model-agnostic methods of eXplainable AI (XAI) can help provide explanations to understand black-box models on NCDs datasets better.

Performance Evaluation of Explainable Machine Learning on Non-Communicable Diseases

In recent times, machine learning is finding many important use cases in the financial services industry. Financial datasets usually pose significant statistical challenges, and hence, traditional econometric methods fail in many practical applications. Though several approaches are suggested and being used, to predict whether a borrower would default on the loan availed, it is still an open challenge. In this work, two approaches are proposed based on LSTM (Long Short Term Memory) along with a hybrid neural network architecture to understand the context between financial transactions and loan defaults. The novelty of the proposed methods is in how they handle structured data and the associated temporal data. Further, the performance of the proposed approaches using debit card transactions and loan application information of customers to predict default is demonstrated. The experiments provided promising accuracies. Bidirectional LSTMs with a hybrid architecture achieve an accuracy of 94%. Hybrid neural network architectures used in this work provide an appropriate direction for making an early warning system through online loan default prediction.

Machine Learning in Finance: Towards Online Prediction of Loan Defaults Using Sequential Data with LSTMs

This paper describes hybrid method for smart house control system, where control is made by training neural network with user habit data and then applying fuzzy rule set and computing with words engine to provide user with explanation why control change to light, heating or ventilation was made. For neural networks two different approaches were tested: classical support vector machine and newly emerged ML.Net framework. Both methods could correctly predict user desired controls with ~99% accuracy, but ML.Net was recommended for further use because it was less selective for the data format, on the other hand SVM required data separation for binary classification thus increasing required number of SVM machines if control areas would expand. Given verbal explanations accurately described current house control situation to the users, thus increasing confidence level for Explainable Artificial Intelligence.

Hybrid Explainable Smart House Control System

Abstract Digitalization and technologization affect numerous domains, promising advantages but also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance implement these technological advances—especially Artificial Intelligence—regulators prescribe high levels of transparency, assuring the traceability of decisions for third parties. Explainable Artificial Intelligence (XAI) is of tremendous importance in this context. We provide an overview of current research on XAI in Finance with a systematic literature review screening 2,022 articles from leading Finance, Information Systems, and Computer Science outlets. We identify a set of 60 relevant articles, classify them according to the used XAI methods and goals that they aim to achieve, and provide an overview of XAI methods used in different Finance areas. Areas like risk management, portfolio optimization, and applications around the stock market are well-researched, while anti-money laundering is understudied. Researchers implement both transparent models and post-hoc explainability, while they recently favored the latter. 

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Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature

For investors seeking solutions to optimize their portfolio of assets to generate profits and minimize losses, choosing a sophisticated model to evaluate the risk of corporate financial distress will be crucial to support their asset management and investment decisions. Both the machine learning and the newly developed deep learning techniques have been employed to construct bankruptcy prediction models for decades. However, applying the deep learning models might increase the predictive accuracy in exchange for losing model interpretability, because the structure and parameters of the model are not easy to provide accountability for investors. In this study, a hybrid approach integrating the decision tree with the deep neural network was proposed to provide a compromise solution for investors. The decision tree was adopted as the primary model to provide explainable ability, while the deep neural network was chosen to improve predictive accuracy. The decision fusion of two models was designed with the compensatory and non-compensatory approaches. The hybrid model was implemented by concatenating the deep neural network to the selected branches of decision tree that perform poor predictive accuracy during model training. The empirical results showed that the predictive accuracy of the deep neural network and the decision tree were 80% and 87% respectively, and the overall accuracy was improved to 91% by the hybrid model.

Tsung-Nan Chou

Papers

An Explainable Hybrid Model for Bankruptcy Prediction Based on the Decision Tree and Deep Neural Network