As the United States rolls out new tariffs, many nations face a common challenge. In negotiations where traditional sectors are pitted against the tech industry, what is the right path? Should a country prioritize rapid development, sacrificing the interests of a relative few? Or should it pay a higher price to safeguard its long-term national security?

In such balancing acts, perfect fairness and universally popular compromises are rarely achievable. The goal, instead, is to build the broadest possible social consensus and prevent sentiments of unfairness from creating political resistance. But how can governments avoid policy backlash and stay aligned with public opinion? Artificial intelligence may offer a solution.

Following the devastating California wildfires that forced nearly 200,000 people from their homes, the state government used an AI-powered tool to identify the most widely supported recovery measures.

The affected residents were a diverse population, with different racial backgrounds, ages, locations, and economic means. Their priorities for rebuilding varied significantly, making it nearly impossible to satisfy everyone.

In response, the California government piloted "Engaged California," an innovative platform where every individual could voice their opinion. The system then identified priorities with the most overlap, and AI synthesized these into viable policy proposals. Initiatives that proved highly polarizing were advanced to a further round of deliberation.

This approach ensures that while the majority guides the general direction of policy, minority groups have the power to block initiatives that are clearly detrimental to them. By ensuring every group's concerns are heard, this process significantly reduces public dissatisfaction. The design goal is for every policy that is implemented to be moderate and enjoy a broad base of support.

The platform works by identifying different demographic groups and the policies that matter most to each. For example, while a smaller number of people might prioritize mental health recovery—making it difficult for the issue to gain traction in traditional budget debates—this system calculates a consensus metric called the "Ethelo Score." This score ensures that proposals are balanced, weighing the breadth of support against the level of contention, thereby reducing the dissatisfaction of minority groups.

Every measure implemented through this mechanism is first filtered by a wide array of community groups, weeding out extreme options. This prevents the rights of specific minorities from being sacrificed for the majority and stops the agenda from being hijacked by the most strident voices. It guarantees that majority needs are met while preserving the community's diversity and inclusivity. Even when opinions are sharply divided, AI's powerful analytics can pinpoint the sources of disagreement between groups and help refine proposals to increase their acceptability.

For instance, when the California government offered mental health support like free hotlines and counseling, public debate on the division of state and federal responsibility became highly polarized. However, the system's analysis revealed that residents—regardless of party or location—viewed this support as a key priority, even if not their top one. This insight successfully forged a consensus that transcended group divides.

The benefit of this method is twofold: it avoids both top-down, unilateral decrees and a "winner-take-all" outcome driven by the loudest groups. Instead, by using AI to synthesize workable solutions, it fosters a culture of dialogue among diverse communities—spanning age, gender, urban-rural divides, and religion. This empowers society to address conflicts, forge consensus, and build a more resilient and unified civil society.