World Solve

As identity, payments, records, and service portals become central to state capacity, failures will produce large-scale disputes. The unresolved problem is setting up fast, fair, and trusted systems to resolve them. This becomes particularly difficult in informal settlements, because public institutions must remain fair, resilient, and accountable while adapting to new risks.

Urban growth, migration, and climate change will outpace traditional master planning. The challenge is designing governance that can revise land use, service delivery, and infrastructure priorities continuously without becoming arbitrary. This becomes particularly difficult in post-conflict institutions, because public institutions must remain fair, resilient, and accountable while adapting to new risks.

Autonomous spacecraft may make split-second avoidance decisions using imperfect data and proprietary software. The unresolved problem is assigning responsibility, proving fault, and compensating affected parties across jurisdictions. 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.

Autonomous spacecraft may make split-second avoidance decisions using imperfect data and proprietary software. The unresolved problem is assigning responsibility, proving fault, and compensating affected parties across jurisdictions. 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 will need near-real-time visibility into commitments, arrears, off-budget liabilities, and contingent risks. The open problem is building financial transparency systems that ordinary citizens and auditors can actually use. This becomes particularly difficult in informal settlements, because public institutions must remain fair, resilient, and accountable while adapting to new risks.

As more government activity moves online, corruption may shift from visible cash handling to invisible data manipulation, access control, and procurement capture. The challenge is detecting abuse in systems that look efficient on the surface. This becomes particularly difficult in informal settlements, because public institutions must remain fair, resilient, and accountable while adapting to new risks.

Public services will increasingly depend on rapidly updated models and decision pipelines. The unresolved problem is keeping these systems lawful, understandable, and corrigible as they evolve faster than formal regulations. This becomes particularly difficult in informal settlements, because public institutions must remain fair, resilient, and accountable while adapting to new risks.

Digital consultation can produce large volumes of participation while still failing to influence decisions. The challenge is creating governance processes where consultation changes outcomes and does not become symbolic theater. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

Future political manipulation may be conducted by large networks of bots, synthetic personas, and microtargeted propaganda. The challenge is detecting and deterring these campaigns without undermining legitimate civic participation. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

The technical problem is coupled with a social allocation problem: production varies, meters may be imperfect, and residents need rules they trust. Tariff design, optimization, and transparent settlement tools could make shared generation investable. 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 forecast uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

The bottleneck is not only parts supply but identification, dimensions, safety, and liability. Open component ontologies, machine-vision matching, and verified refurbishment networks could create new revenue for local technicians. 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 repair 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.

Digital contracts can potentially be modified after signing in ways that aren't always immediately detectable without specialized forensic review. Building this verification directly into standard contract systems remains incompletely adopted.

Contracts translated using automated tools can sometimes contain subtle errors that create legal ambiguity between parties speaking different languages, and verification processes haven't fully adapted to catch these risks. Closing this legal exposure gap remains unresolved.

Robocalls and spam calls continue to reach users despite various blocking technologies, partly because scammers can spoof caller information and operate across international boundaries where enforcement is inconsistent. A fully effective, universal solution remains unbuilt.

Techniques that let multiple parties jointly compute something without revealing their private data to each other exist, but they remain too slow and complex for widespread practical adoption. Making this both secure and fast enough remains an active research challenge.

Companies increasingly build products using AI models developed by others, but there's no consistent, scalable way to verify those models are safe and reliable before integration. This creates hidden risk throughout the growing AI supply chain.

As software grows to meet more complex user needs, it also grows harder for any single team to fully understand every part of it, creating hidden risks. Balancing capability with true comprehensibility remains an unsolved software engineering challenge.

AI models trained on real-world data often absorb and sometimes amplify existing societal biases, and current bias correction methods only partially address the problem. A more complete solution remains an active and unresolved area of research.

Fake or substandard electronic parts sometimes slip into supply chains for critical equipment, including medical devices and vehicles, and current detection methods are neither fast nor cheap enough to catch every case. This creates hidden safety risks across many industries.