Is the future of governance written in code rather than law?

Society · Published 18 October 2026 Swipe more like this →

In short: From 1960s Soviet economic networks to modern smart cities, the concept of 'algocracy' is moving from theory to reality. As algorithms begin to automate everything from judicial decisions to tax enforcement, we must ask: are we being governed by transparent laws or invisible, self-executing code?

The concept of 'government by algorithm'—or algocracy—describes the use of computational tools to influence or regulate sectors like law enforcement, transportation, and land registration. While often associated with modern big data, the seeds were sown decades ago. In 1962, Soviet scientist Alexander Kharkevich proposed Project OGAS, a computer network intended to control the national economy. This sparked significant concern in the West; US analyst Arthur M. Schlesberg Jr. warned that by 1970, the USSR might possess a production technology managed by self-teaching, closed-loop computers.

This transition toward automated governance is not merely about efficiency; it represents a fundamental shift in how power is exercised. In 2006, attorney Lawrence Lessig famously noted that 'Code is law,' suggesting that the architecture of cyberspace, pushed by both government and commerce, is creating a system of highly efficient, perfected control. Unlike traditional bureaucracy, which relies on legal-rational regulation, or markets, which rely on price, algocratic systems use information technologies to constrain human participation in public decision-making.

Today, the application is widespread. In the US, a Stanford University study found that 45% of federal agencies experimented with AI and machine learning up to 2020. These tools range from the ATLAS software used by the Department of Homeland Security to scan 16.5 million records for potential fraud, to the NarxCare system, which uses machine learning to generate overdose risk scores. However, this 'intelligent approval' in smart cities faces critical challenges, including a lack of transparency, weak error correction, and the potential for decisions to become inconsistent with due process.

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