Algorithm Governance in Social Welfare: A Narrative Review of Data-Driven Decision-Making in Social Work and Policy

Abstract

This paper discusses the increased use of data analytics and algorithmic decision-support systems in the social sciences, focusing specifically on the implications of such systems to governance of social welfare in Pakistan. It also critically assesses the opportunities, the risk and the policy considerations of the digital transformation of welfare administration. The paper is conducted in the form of a narrative review of interdisciplinary sources on the topics of social policy, algorithmic governance, data ethics, and digital public administration. It summarizes the world evidence and puts the findings into context in the emerging digital welfare environment in Pakistan. Data analytics can provide a substantial opportunity in enhancing precision in targeting, administrative efficiency, and evidence-based policy making in social protection program of Pakistan. Nevertheless, algorithmic bias, data quality constraints, digital exclusion, privacy risks, and lax regulatory oversight risks can cause social inequalities to worsen. Unless there are strong governance systems, automated systems may work to strengthen structural discrimination instead of reducing poverty. The paper suggests reinforcing laws on data protection, providing algorithmic impact testing, investing in institutional capacity, and human intervention in the welfare decision-making process. To protect the equity and the trust of people, participatory and context-sensitive modes of governance are necessary.

Keywords: AI Governance; Data Analytics; Data Ethics; Social Welfare Policy, Predictive Decision Support System