World Solve

Healthcare equipment often uses sterile, complex, or regulated components that are difficult to reuse. Future systems need to reduce waste while preserving infection control and clinical reliability. Affected communities must have meaningful control over data, revenue, deployment decisions, and the right to refuse harmful applications. 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.

Urban planning models often omit informal homes, businesses, infrastructure, and social networks. The unresolved problem is creating useful digital representations without enabling eviction, surveillance, or land speculation. 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.

Addressing police often failing to help victims is critical for achieving the 2030 Agenda. Current efforts fall short in scale, speed and equity, leaving the most vulnerable behind. Any viable solution must function in low-resource settings with limited specialists, transport, laboratory capacity and broadband. Proposals should specify quantifiable biological, social or environmental outcomes, openly discuss potential risks, and present a realistic pathway for long-term maintenance and local ownership beyond short research pilots.

Works that visibly deteriorate according to how often they are ignored accelerate cultural loss. The problem is supporting art maintenance without turning public attention into a survival requirement for cultural objects.

Warnings often fail because people lack transport, trust, shelter, money, or authority to act. The unresolved problem is linking forecasts to pre-agreed decisions, resources, and responsibilities at local level. 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.

Building codes often use historical temperature, rainfall, and wind data. Future standards must remain useful across uncertain climate trajectories without making housing unaffordable. 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.

Claims about quantum advantage often depend on narrow benchmarks or idealized assumptions. The challenge is creating reproducible measurements that identify when quantum systems deliver meaningful benefits over classical alternatives. 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.

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.

Frontline workers often face incomplete records, unreliable connectivity, and high consequences for missed deterioration. Decision-support models must be calibrated locally, auditable, and paired with referral logistics rather than treated as standalone AI. 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 verify uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Teachers cannot manually inspect every error pattern, while generic adaptive apps often optimize clicks rather than mastery. Compact knowledge models and teacher-controlled workflows could make personalization affordable and pedagogically accountable. 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.

Curb demand changes by hour and street, but regulations are often static and enforcement is inconsistent. Auctions, reservation systems, and fairness constraints could improve throughput while protecting accessibility and local commerce. 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 preserve uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Competence is often real but invisible to employers because certificates measure course completion rather than performance. Practical assessments, portable evidence, and trusted local validators could improve matching without forcing workers into expensive retraining. 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 allocate uncertainty, incentives, or resource constraints; entrepreneurship can turn a reliable workflow into a viable service.

Bad actors can spread coordinated campaigns across several platforms at once, and current detection systems often work in isolation per platform rather than recognizing the broader cross-platform pattern. Building this kind of coordinated detection remains an unresolved industry challenge.

Automated scraping tools can collect publicly visible personal data from social media profiles far faster and more comprehensively than manual methods ever could, often for uses the original poster never intended. Meaningful technical or legal limits on this practice remain inconsistent.

Networks of compromised devices, often unknowing to their owners, can be rented out cheaply on illicit markets for attacks like large-scale website disruptions. Dismantling these persistent botnet infrastructures remains an ongoing cybersecurity challenge.

Once login credentials are leaked in a breach, they often remain accessible and exploitable for attackers on illicit marketplaces for years afterward, long after the original breach was reported. Meaningfully reducing this lingering exposure remains unresolved.

Many connected devices become permanently vulnerable once their manufacturer stops issuing security updates, yet the devices often remain in active use for years afterward. A sustainable way to keep abandoned devices secure remains unresolved.

Data brokers can aggregate information from many different sources to build surprisingly detailed profiles on individuals, often without those individuals ever being aware or having consented. Meaningful regulation and technical limits on this practice remain inconsistent globally.

Accessibility efforts often focus more on physical and sensory disabilities than on cognitive accessibility, leaving many users with learning or cognitive differences underserved by mainstream digital design. Closing this specific accessibility gap remains an underaddressed challenge.

Developers often only discover accessibility problems after a product has already launched, since automated tools can't yet catch every meaningful barrier during the build process itself. Building this detection earlier into development workflows remains incomplete.