Differentiable Optimization with Daoist Principles: A Fair Credit Scoring Framework under Socioeconomic Bias Constraints

Abstract

This research paper explains a very special interdisciplinary answer based on incorporating Daoist principle of Wu Wei (non-coercive action) and variousiable optimization procedures to derive a just and dynamic credit scoring system. As a type of quantitative research design, structured questionnaire, consisting of 400 respondents was used to collect primary data and analyze them using descriptive statistics, correlation, regression analysis and testing hypothesis. The findings show that Wu Wei optimization has a positive influence on enhancing the fairness at the declining socioeconomic bias during credit scoring mechanisms on a large scale. The Daoist approach compares with the traditional constraint-based approaches may stabilize fairness with strict correction, but instead facilitate the idea of dynamic equilibrium, minimal intervention and self-correcting mechanisms, which facilitates the fairness which develops in a naturally condition without compromising the predictive performance. The research would contribute to the literature with both philosophical dimension of taking into account the algorithmic fairness and by demonstrating that it is possible to use principles of Daoism-based ethics to manage the design of technical models effectively. It also yields practical implications to developers of FinTechs and policymakers with assistance to adjust files to adaptative, transparent, ethically-motivated AI systems. Overall, in the given research the notion that an amalgamation of Daoist philosophy with the existing framework of optimization is a viable and holistic approach to the issue of bias eradication in credit scoring is proven to be correct.