Algorithmic Social Justice and Gender Equality: Child Labour, Welfare Inequalities and Access to Justice in the Era of Intelligent Governance

Abstract

The increasing implementation of algorithmic governance in public welfare institutions is transforming how states address social inequalities, particularly in the areas of child welfare and the justice system. While artificial intelligence (AI) is often praised for its potential to enhance efficiency, predictive accuracy, and scalability, there is mounting evidence that these systems may reinforce or exacerbate existing gender and socio-economic disparities. This research examines the connection between algorithmic decision-making, gender equity, vulnerability to child labour, and access to justice.

This study utilizes a mixed-method research design that includes secondary datasets, policy analysis, and comparative case studies from both developing and developed welfare systems. Research indicates that algorithmic tools used for welfare targeting, risk assessment, and service allocation often contain historical biases within their training data. These biases disproportionately impact marginalized populations, particularly girls, resulting in higher rates of exclusion errors, increased risk of misclassification, and limited access to protective services.

The study presents an Algorithmic Equity Model that emphasizes transparency, bias auditing, the inclusion of gender-sensitive data, and governance based on child rights. This model aligns with global development priorities and underscores the need for regulatory oversight and ethical deployment of AI in welfare systems. The research contributes to ongoing discussions about equitable digital governance and provides policy recommendations for creating justice-oriented, inclusive algorithmic systems.