Artificial intelligence (AI) has emerged as a transformative tool in law enforcement, with predictive policing drawing increasing attention for its potential to improve crime prevention. In Nigeria, however, its application raises pressing ethical concerns around fairness, privacy, and accountability. This study examined the operational and ethical implications of predictive policing in Nigerian urban centers, with the objective of assessing its effectiveness, risks, and institutional readiness. Anchored in Technological Determinism, Social Constructivism, and Procedural Justice Theory, the study adopted a literature-based research design, synthesizing empirical works from Nigerian and global contexts. Data were drawn from peer-reviewed studies through purposive selection, thematically analyzed, and interpreted against the theoretical frameworks. Findings show predictive policing can reduce certain crimes and assist in hotspot mapping, yet these operational benefits are offset by serious challenges: poor data quality fosters algorithmic bias against marginalized groups; opaque decision-making processes weaken accountability; and limited legal safeguards expose citizens to privacy violations and surveillance overreach. Moreover, mistrust of law enforcement in Nigeria intensifies public resistance to predictive technologies. The study concludes that predictive policing in Nigeria will only succeed under conditions of transparent governance, fairness audits, robust data infrastructure, and active community engagement. It contributes localized insights to a debate often dominated by Western perspectives, highlighting the balance between technological innovation and ethical governance in fragile democracies.

Predictive policing, Artificial intelligence, Algorithmic bias

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