Algorithmic justice and ethical governance in artificial intelligence: a conceptual insight and further research suggestions

Purpose The growing use of algorithmic decision-making has generated a raging scholarly and policy debate on the ethical dimensions of artificial intelligence (AI). Researchers call for the creation of ethical governance systems – including regulatory frameworks, transparency platforms, algorithmic audit systems and accountability mechanisms. Yet, there is limited conceptual understanding of how algorithmic justice principles could be systematically embedded in those governance frameworks. This study aims to develop an integrated framework that explains the ethical challenges associated with AI and how governance mechanisms and algorithmic justice principles can be leveraged to promote responsible and equitable AI deployment. Design/methodology/approach The study uses a conceptual literature analysis grounded in a structured thematic synthesis of 23 scholarly papers published between 2016 and 2026, focused on AI ethics, algorithmic bias, algorithmic justice and responsible AI governance. The selection process was guided by transparent inclusion and exclusion criteria, consistent with PRISMA 2020 principles. Findings The synthesis shows that algorithmic decision-making systems produce unintended ethical consequences – including algorithmic bias, lack of transparency and poor accountability mechanisms – when deployed in socially sensitive environments. A distinctive conceptual outcome is the insufficiency of technical fairness interventions in the absence of robust institutional governance, calling for a systemic, governance-first approach wherein algorithmic justice principles are embedded as foundational institutional requirements. Research limitations/implications This conceptual study opens avenues for empirical research on the ethical challenges of algorithmic decision-making across sectors such as finance, health care, public administration and digital governance. Future research should examine how algorithmic risks manifest in specific institutional contexts and evaluate multi-stakeholder governance models. Practical implications Regulatory bodies and policymakers should design comprehensive frameworks that foster ethical AI management – encompassing transparency, accountability and fairness requirements. Algorithm impact assessments and auditing systems should become standard governance instruments before AI deployment in socially sensitive domains. Social implications Embedding algorithmic justice within governance structures is a social justice imperative. When AI systems reproduce historical biases without adequate oversight, they deepen structural inequalities and undermine democratic inclusion. Equitable access to fair and transparent algorithmic decision-making is essential for preserving the rights of marginalised communities. Originality/value This study advances the existing literature by developing an integrated conceptual framework that systematically embeds algorithmic justice principles within institutional governance structures. Uniquely, it positions institutional governance as the primary mediating mechanism between abstract ethical commitments and the actual ethical performance of AI systems, bridging technology ethics theory and socio-technical systems theory. It distinguishes itself from existing frameworks by incorporating organisational meaning-making and posthumanist perspectives on algorithmic authority.