World Solve

Private companies may increasingly run water, transport, energy, communication, and housing systems with public consequences. The open problem is designing regulation that ensures reliability, accountability, and public recourse when essential services fail. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Quantum randomness could improve high-stakes public processes, but its value depends on independently verifiable hardware, transparent protocols, and protection against manipulation at the measurement and software layers. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

As benefits move online, errors can become opaque and difficult to reverse. The challenge is creating fast, accessible grievance systems that help ordinary people correct eligibility mistakes without requiring legal expertise. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

States may invest in national AI systems for education, law, defense, planning, and public administration. The unresolved problem is ensuring such systems remain bounded by law, reviewable by citizens, and resistant to capture by narrow interests. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Digital commerce, cloud services, and AI platforms can generate value across borders while escaping clear tax authority. The challenge is creating fair tax rules that smaller states can enforce without being excluded from the digital economy. The challenge is especially acute for low-resource administrations, where legacy systems, coordination failures, and weak oversight can magnify harm.

Digital commerce, cloud services, and AI platforms can generate value across borders while escaping clear tax authority. The challenge is creating fair tax rules that smaller states can enforce without being excluded from the digital economy. The challenge is especially acute across fragmented municipalities, where legacy systems, coordination failures, and weak oversight can magnify harm.

Many institutions cannot inventory where vulnerable cryptography is used across old software, embedded devices, archives, and vendor systems. The future challenge is completing secure migration before quantum-capable attackers can exploit long-lived encrypted data. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Governments currently discount long-term harms because future people cannot vote today. The open problem is creating formal institutions that represent future citizens in budget, infrastructure, climate, and technology decisions. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

As heat, flooding, drought, and erosion intensify, some communities will need to move. The challenge is designing relocation policy that protects land rights, livelihoods, identity, and consent rather than turning climate adaptation into forced displacement. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Frontier AI may become essential infrastructure controlled by a small number of governments or companies. The unresolved task is developing ownership, access, competition, and public-interest models that prevent private or geopolitical concentration from determining global outcomes. The design must remain functional when standards, trade routes, data access, and diplomatic cooperation are unreliable. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Disasters, conflicts, and public-health crises force governments to buy quickly, often with reduced oversight. The open problem is building emergency procurement systems that are fast enough for crises but transparent enough to prevent leakage, favoritism, and fraud. The challenge is especially acute across fragmented municipalities, where legacy systems, coordination failures, and weak oversight can magnify harm.

Political communication will increasingly include deepfakes, voice cloning, and AI-generated persuasion at scale. The challenge is developing election rules, platform standards, and enforcement systems that protect speech while limiting manipulation. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Future AI systems may improve software, research workflows, and model-training pipelines faster than institutions can review them. The open problem is designing international oversight that supports beneficial progress while preventing uncontrolled capability escalation. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Future governments may depend on private firms for cloud services, AI tools, digital identity, and critical infrastructure software. The unresolved problem is designing procurement rules that preserve interoperability, portability, and long-term public control. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

As machine learning enters welfare, policing, housing, tax, and licensing systems, bias can become harder to detect and contest. The open problem is creating enforceable standards for fairness, appeals, and independent review across all public-sector uses of automated decision-making. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Future states will need identity systems that work for migrants, displaced people, informal workers, and citizens missing formal paperwork. The challenge is building identity infrastructure that expands access without becoming a surveillance or exclusion tool. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Future infrastructure will increasingly rely on systems capable of making decisions without immediate human supervision. The unresolved problem is creating architectures that can prove what they did, explain uncertainty, fail safely, and remain controllable during abnormal conditions. The solution must work with limited capital, intermittent services, and local maintenance capacity. The research opportunity is to define measurable success criteria, test the approach in realistic settings, identify failure modes, and create a pathway from prototype to accountable deployment.

Governments will increasingly rely on automated systems to allocate benefits, flag risks, prioritize inspections, and recommend enforcement. The unresolved problem is creating governance structures that make these systems transparent, contestable, and accountable without disabling public service delivery. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Utility data are fragmented, pressure changes are poorly localized, and repairs compete with emergency work. Low-cost sensing, probabilistic fault localization, and outcome-based maintenance could let local operators prioritize the few failures that matter most. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with measure uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Many consultation processes collect comments but provide no traceable response or rationale. Structured argument maps, versioned policy records, and privacy-aware participation analytics could improve accountability without reducing deliberation to popularity. The opportunity is intentionally human-scale: a cooperative, clinic, workshop, school, or municipal team could pilot it within one locality and measure outcomes before expanding. Mathematical modeling can help with coordinate uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.