Ethical Frameworks and Machine Learning for Effective Poverty Alleviation: A Logistic Regression Approach

Abstract

Poverty remains a significant challenge for many developing nations, including Pakistan, where the rural population continues to suffer from limited access to essential resources. This study applies logistic regression models to examine the role of ethical principles in poverty alleviation efforts. The focus is on understanding how key ethical interventions, such as avoiding extravagance, religious beliefs, vocational training, microfinance, and Islamic loans, impact the likelihood of poverty reduction in low-income households. A dataset consisting of various socio-economic factors was analyzed, revealing that ethical principles were the strongest predictors of successful poverty alleviation programs. The logistic regression model achieved a high accuracy rate of 75%, highlighting the importance of ethical moderation in fostering community support for poverty alleviation. Among the ethical principles, those promoting frugality and moderation stood out as the most influential, leading to a 54% success rate in poverty reduction. Vocational training emerged as a key factor, contributing significantly to improved employment and income outcomes. Conversely, religious beliefs and resource allocation, while important, showed a minimal impact on the model's accuracy. These findings offer valuable insights for policymakers, suggesting that incorporating ethical frameworks and expanding vocational training programs can play a pivotal role in reducing poverty. The research underscores the potential of combining machine learning techniques with ethical principles to create more effective, data-driven poverty alleviation strategies.