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Paper short abstract

This paper examines how data-driven policing shapes public safety governance in Nigeria and its implications for justice and accountability. Although advanced AI systems are limited, security agencies rely on crime data, surveillance, and risk profiling to guide policing. The study shows that these

Paper long abstract

This paper examines how data-driven security practices are shaping policing and public safety governance in Nigeria, and what this means for justice, rights, and accountability. While Nigeria does not widely deploy advanced artificial intelligence or predictive policing algorithms, security agencies increasingly rely on crime statistics, digital records, surveillance tools, and risk profiling to guide policing decisions. These practices reflect predictive logics similar to those embedded in algorithmic systems elsewhere.

Using a qualitative case study approach, the study analyses how data-based security decisions affect marginalized communities, particularly young people and low-income urban populations. It shows that data-driven policing often reproduces existing inequalities by reinforcing suspicion, selective enforcement, and the criminalization of poverty. Limited transparency, weak oversight, and the absence of clear accountability mechanisms further raise concerns about digital control rather than justice.

The paper contributes to debates on algorithmic justice by demonstrating that justice risks associated with predictive policing are not limited to advanced AI systems. Instead, they also emerge in low-technology contexts where data is used to justify coercive security practices. The study argues for justice-oriented approaches to public safety that prioritize accountability, community participation, and human security over techno-solutionist and punitive models. By grounding the analysis in the Nigerian context, the paper offers a Global South perspective on how digital futures of security governance can be reimagined beyond surveillance and control.