AI explains how drug wars are lost | Drug WarRant

Artificial Intelligence appears at first glance to be an obvious choice for sourcing new strategies to win the war on drugs. AI might have worked in this regard if only drug wars possessed some kind of logical or realistic narrative. Human nature and the arbitrary limits posed by mistaken perceptions about drugs and their use get in the way of predicting exact outcomes.

The U.S. government questioned AI to determine its likelihood for winning drug wars. The feedback has not been encouraging. AI admits the short answer is AI cannot “win” the drug war.

Use of AI can temporarily disrupt drug flows and make public health responses more effective, but the economics of prohibition still rule. Traffickers quickly adapt to AI incursions by using it to protect their own operations. The drug war rapidly becomes a tech arms race between drug smugglers and the governments prosecuting them.

Using a strict prohibitionist model, AI detection, interdiction and predictions of drug smuggling that repress supply networks also intensify the harms of prohibition. There is always a risk to civil liberties since race and class inequalities in drug policing and incarceration still get amplified with action plans. This effect is encountered when a drug like raw opium can become something that benefits people but can also give nations the ability to control supply chains, bend the world, or break foreign leaders to their advantage. This was the case for the first British Opium War occurring between 1839 and 1842 with China. Disputes over the opium trade led to China’s efforts to suppress the business which ultimately resulted in a British military victory over China. The Treaty of Nanking ceded Hong Kong to Britain. It also opened several Chinese ports to foreign trade.

A drug decriminalization or harm reduction model is better aligned with AI for improving drug enforcement results. Decriminalization reduces prohibition harms and improves triage, but the illegal markets and violence persist.

A well-regulated market and health-centered decriminalization model using AI that reduces ODs, disease, and violence is where AI can prove powerful. AI is good at monitoring complex systems, spotting risk, and optimizing health outcomes rather than arrests. No one wins the drug war in this model if their primary aim is to eliminate rather than displace a popular drug from its marketing niche.

The displacement of marijuana through state legalization and federal rescheduling from Schedule I to Schedule III are examples of how a regulated market allows societies to adapt to a drug in ways that eliminate arrests and wars while simultaneously helping to keep drugs out of the hands of children and adolescents. Health concerns are achieved in part by drying up illegal markets while accepting legalized products from sources that can create and provide child protection packaging in addition to selling an uncontaminated, cheaper item; one that replaces, defies, or deters illicit competitors while recognizing any needed marketing or consumer safeguards.

A question remains. If AI doesn’t think it can win a drug war then what possibility do humans have of winning it? Drugs tend to win drug wars, not people. The only things left standing after a major war on the drug market are often a lot of drugs.

Drug victories are also wakeup calls to every opportunistic politician and political appointee who favored strict prohibition as a political cover or as a means of career maintenance and advancement. In the future they might want to follow Bill Clinton’s advice and “make change your friend.” Failing to deconstruct prohibition and its harms means they face serious opposition from future political challengers who are certain to use drug war concerns and AI to defeat them.

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