By Hassan Husaini mni
Last week, I laid out the historical record of regional policing and the documented patterns of gubernatorial influence over federal police that raise legitimate concerns about state police. I also urged you to pause and reflect. This week, I address the practical, fiscal, ethnic, and technological dimensions of the state police proposal—and, crucially, what genuine police reform should look like. I write with respect for the difficulty of your task, but also with a sense of urgency. The decisions you make will affect every Nigerian.
Let us begin with fiscal realities. The cost of establishing state police is staggering. A technical committee established by the Inspector-General of Police estimated that setting up state police across the federation will cost between N589 billion and N813 billion over five years. To put this in perspective, the Kulen Allah Cattle Rearers Association of Nigeria estimated that a single state with 15 local governments would require approximately N10 billion in initial setup costs and about N3 billion monthly for salaries and operations. These figures are not abstract. They represent real financial commitments that must be met from state budgets.
Where will these funds come from? This is the question that demands an honest answer. It is a matter of public record that many states have struggled to meet salary obligations without federal support. Reports from the Revenue Mobilisation Allocation and Fiscal Commission and BudgIT Foundation indicate that over 20 states have faced challenges paying salaries, and local government allocations have often times been diverted away from their intended purposes. If states are already stretched to meet basic governance obligations, how can they suddenly find billions of naira to fund police forces? This is not an accusation of bad faith; it is a practical acknowledgment of fiscal constraints.
The concern is not merely about the initial setup. It is about sustainability. Monthly salaries, equipment maintenance, training, and operational costs will continue indefinitely. States with smaller internally generated revenues will struggle to sustain these forces. The likely outcome is that funds will be diverted from education, healthcare, and infrastructure to pay for policing. Nigerians in poorer states may find that their children’s schools lose funding while their governors maintain police forces.
There is, however, an even graver consequence that must be considered: the danger of unpaid or delayed salaries. History has shown that when police officers are not paid on time, the consequences can be catastrophic. Unpaid police officers are susceptible to corruption, bribery, and extortion as they seek alternative means of survival. More dangerously, they can become unpredictable, volatile, and prone to industrial actions that leave communities completely unprotected. In extreme cases, disgruntled officers have
been known to collude with criminals or turn their weapons against the very citizens they are sworn to protect. The Nigeria Police Force itself has experienced episodes of salary delays that led to protests, strikes, and breakdowns in law enforcement. If the federal government, with its vastly superior resources, has struggled to guarantee timely payment of police salaries, how much more will states -many of which already owe months of salary arrears to their own civil servants—manage to pay state police officers consistently? The grim reality is that state police officers may find themselves unpaid for months, turning them into desperate men and women with arms and uniforms, a recipe for disaster.
Now consider the ethnic dimension. Nigeria is a federation of over two 250 ethnic groups. The North-Central states— Plateau, Benue, Nasarawa, Niger, Kogi, Kwara—are among the most diverse. In Benue, for example, the Tiv are the majority, but there are significant Idoma, Igede, and other minority populations. If a Tiv governor were to recruit predominantly Tiv police officers, the Idoma and Igede minorities might understandably fear that the police would not protect them even-handedly in communal conflicts. In Plateau, the long-running conflict between Berom, Afizere, and Anaguta communities on one hand and HausaFulani communities on the other has already claimed many lives. A state police force perceived as aligned with one side could worsen tensions. The Nigeria Police Force, despite its flaws, operates under a federal recruitment system that attempts to balance ethnic representation; officers are rotated across states, limiting local capture. State police would be recruited locally and accountable locally. The potential for perceived or actual ethnic bias is a concern that deserves honest discussion.
There is, however, a more insidious dimension to this ethnic concern that has received far too little attention: the intersection of ethnicity and technology.
State police forces, if established, will inevitably adopt modern investigative tools—facial recognition systems, predictive policing algorithms, digital forensics, and AI-driven surveillance platforms. These technologies are already being deployed by law enforcement agencies globally, and Nigeria will not be an exception. But here is the danger: these technologies are not neutral. They carry embedded biases that reflect the data on which they are trained. Facial recognition systems, for instance, have been shown to be significantly less accurate for individuals with darker skin tones, leading to potential misidentification and wrongful arrest. A 2019 study by the United States National Institute of Standards and Technology found that African and Asian faces were between 10 and 100 times more likely to be misidentified than white faces.
In a state police system dominated by a single ethnic group, this technological bias becomes a weapon. The majority ethnic group controlling the state police would have access to surveillance databases, forensic tools, and AI systems that they could use—deliberately or inadvertently—to shield members of their own community from prosecution while targeting minorities. Consider a crime committed in a state where the governor and police leadership belong to the majority ethnic group. The suspect is from a minority community. The state police deploy facial recognition technology to identify the perpetrator. But the technology, biased by design, misidentifies the minority suspect while failing to recognise the majority suspect whose image is more accurately processed. The minority suspect is arrested and prosecuted. The majority suspect walks free. This is not science fiction. This is the documented reality of algorithmic bias.
Hassan Husaini mni can be reached via hassanhusainil009@gmail.com, 08027000115