World Solve

The problem of trust in elections falling continues to affect hundreds of millions of people and undermines progress on multiple Sustainable Development Goals. Existing responses remain fragmented, under-resourced and poorly adapted to local contexts. A strong solution must operate effectively under severe constraints of skilled personnel, infrastructure, electricity and connectivity. It should define clear measurable outcomes, identify possible unintended harms, and demonstrate how the intervention can be sustained by local institutions long after initial funding ends.

People increasingly need trusted identity across migration, finance, education, health, and employment systems. The challenge is preventing fragmented credentials, exclusion, surveillance, and corporate dependency. 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.

Addressing trust in police remaining low 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.

Traditional credentials are too slow for rapidly changing technical work, while unverified short courses create noise. Future education needs trustworthy, modular, employer-connected, and worker-controlled skill validation. 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.

public trust in experts falling remains one of the most urgent challenges of our time. Systems currently in place are inadequate for the scale and complexity of the problem. A successful approach must work where resources are scarcest and demonstrate clear, measurable results. It should identify possible harms, respect local agency, and show how the solution can be maintained and expanded by the communities it is meant to serve.

Decision-makers lack integrated, high-resolution models of ocean chemistry, biology, currents, and human activity. The open problem is building trustworthy systems that support decisions without creating false confidence from incomplete data. The solution must produce useful coordination and accountability while minimizing collection of sensitive personal and behavioral data. 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.

Decision-makers lack integrated, high-resolution models of ocean chemistry, biology, currents, and human activity. The open problem is building trustworthy systems that support decisions without creating false confidence from incomplete data. 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.

Decision-makers lack integrated, high-resolution models of ocean chemistry, biology, currents, and human activity. The open problem is building trustworthy systems that support decisions without creating false confidence from incomplete 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.

Social bonds structured as time-limited agreements with automatic termination notices can undermine trust and long-term support networks. The problem is allowing intentional relationship boundaries without making every connection provisional and transactional.

Removing debris may require technologies capable of approaching, manipulating, or disabling other spacecraft. The challenge is creating verification and trust mechanisms so debris removal does not become covert anti-satellite capability. 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.

When digital systems fail, public trust in records, payments, and eligibility can collapse. The challenge is designing governance that can preserve continuity, recover data, and verify truth across technical interruptions. This becomes particularly difficult in informal settlements, because public institutions must remain fair, resilient, and accountable while adapting to new risks.

If facial appearance can be altered continuously through filters or implants, both social recognition and security systems lose stable anchors. The problem is maintaining trustworthy identity verification and human connection without requiring permanent biometric locks that themselves create new risks.

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.

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.

AI can generate convincing but fake expert testimony, reports, and analysis. The open problem is building public systems that preserve trust in real expertise while making fabricated authority easier to detect. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

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.

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.

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.

Networks of AI-assisted fake dating profiles can be deployed at scale to build trust with vulnerable users before extracting money or personal information. Detecting these coordinated networks before real harm occurs remains an unresolved platform challenge.

Tutoring platforms can be targeted by individuals using AI-generated fake credentials to appear more qualified than they actually are, and verification systems don't always catch this convincingly fabricated background information. Closing this trust gap remains an unresolved platform challenge.