Ethically Responsible Machine Learning in Fintech

Maryan Rizinski · Hristijan Peshov · Kostadin Mishev · Lou T. Chitkushev · Irena Vodenska · Dimitar Trajanov

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Ethics and Social Impacts of AI Ethics in Business and Education Explainable Artificial Intelligence (XAI)

Abstract

Rapid technological developments in the last decade have contributed to using machine learning (ML) in various economic sectors. Financial institutions have embraced technology and have applied ML algorithms in trading, portfolio management, and investment advising. Large-scale automation capabilities and cost savings make the ML algorithms attractive for personal and corporate finance applications. Using ML applications in finance raises ethical issues that need to be carefully examined. We engage a group of experts in finance and ethics to evaluate the relationship between ethical principles of finance and ML. The paper compares the experts’ findings with the results obtained using natural language processing (NLP) transformer models, given their ability to capture the semantic text similarity. The results reveal that the finance principles of integrity and fairness have the most significant relationships with ML ethics. The study includes a use case with SHapley Additive exPlanations (SHAP) and Microsoft Responsible AI Widgets explainability tools for error analysis and visualization of ML models. It analyzes credit card approval data and demonstrate that the explainability tools can address ethical issues in fintech, and improve transparency, thereby increasing the overall trustworthiness of ML models. The results show that both humans and machines could err in approving credit card requests despite using their best judgment based on the available information. Hence, human-machine collaboration could contribute to improved decision-making in finance. We propose a conceptual framework for addressing ethical challenges in fintech such as bias, discrimination, differential pricing, conflict of interest, and data protection.

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References

2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 1983World Economic Outlook 2017Meaningful information and the right to explanation 2019The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform 2017Ten simple rules for responsible big data research 2018The Jobs That Artificial Intelligence Will Create 2019On Fintech and Financial Inclusion 2019Flexibly Fair Representation Learning by Disentanglement

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