Soft Inequality

Soft inequality research focuses on understanding and mitigating disparities in resource allocation and outcomes, moving beyond simple quantitative measures to encompass nuanced aspects like intersectional disadvantages and the impact of algorithmic bias. Current research employs diverse approaches, including latent class analysis to identify intersecting inequalities, machine learning models to characterize infrastructure quality disparities, and modifications to existing algorithms (e.g., Gaussian processes) to incorporate fairness constraints. This work is significant for its potential to inform the development of fairer and more equitable systems across various sectors, from healthcare and resource allocation to hiring practices and urban planning, by providing data-driven insights into the complex interplay of factors contributing to inequality.