World Solve

Lifts that prioritise or restrict floors according to behavioural scores create physical segregation inside buildings. The problem is designing vertical mobility that remains accessible and non-discriminatory regardless of any scoring system in use.

If individuals can alter gravity in their private environments, public areas must still enforce uniform conditions for safety and fairness. The challenge is preventing spillover effects, health disparities, and social stratification based on access to customized physical laws.

AI agents may perform tasks once done by contractors, analysts, translators, and assistants. The challenge is creating rules for liability, taxation, attribution, and market fairness when human labor and machine labor overlap. The challenge is especially acute across multiple jurisdictions, where legacy systems, coordination failures, and weak oversight can magnify harm.

AI agents may perform tasks once done by contractors, analysts, translators, and assistants. The challenge is creating rules for liability, taxation, attribution, and market fairness when human labor and machine labor overlap. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.

AI systems trained on globally diverse data may encode incompatible assumptions about fairness, authority, family, privacy, and acceptable risk. The challenge is creating pluralistic evaluation methods that protect universal rights without imposing one culture's values as a universal standard. 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 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.

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.

Automated pricing systems can sometimes raise prices sharply during shortages or emergencies based purely on demand signals, without regard for fairness or necessity. Building ethical guardrails directly into these systems remains inconsistently implemented.

Software that predicts where crime might occur or who might commit it is being adopted by police departments with little independent oversight of its accuracy or fairness. This leaves significant power in tools that haven't been thoroughly vetted for bias.

Voting power at bodies like the IMF still roughly reflects the economic order from decades ago, giving less voice to countries that now represent a huge share of the world's population. This mismatch fuels distrust in the fairness of global rule-making.

Predictive policing tools can reflect and reinforce biases present in historical policing data, raising fairness concerns even when the tools reduce some crime. Whether their benefits ethically outweigh this risk remains contested.

Some jurisdictions already use AI risk assessments to help judges decide sentences, raising concerns about fairness, bias, and accountability. Whether machines should have this level of influence over human freedom remains ethically unresolved.

Immigration policy varies enormously between countries, with no widely accepted standard for what fairness or humane treatment actually requires. This leaves migrants' treatment largely dependent on which country they happen to reach.

Simple geometric fairness questions like this remain surprisingly open for many classes of shapes, especially in three or more dimensions. Solving them has practical relevance to resource division and computational geometry.

Arrow's Impossibility Theorem proved certain fairness conditions can't all hold at once for common voting systems, but the full landscape of tradeoffs across all possible systems remains an active research area. This has real implications for the design of democratic institutions.

Fair division theory has strong results for two-party negotiations, but multi-party fairness guarantees become significantly harder to prove in full generality. This unresolved area connects mathematics with real-world conflict resolution.

Competition can drive genuine excellence and innovation, but it also guarantees that some participants will predictably fail through no unique fault of their own. This unresolved tension underlies debates about fairness in education, employment, and markets.

Loyalty to friends, family, or groups is widely valued, but it can sometimes require favoring those close to us over what impartial fairness would otherwise demand. This unresolved tension shapes debates over nepotism, favoritism, and personal ethics.

Enhancing traits like intelligence or appearance raises questions about consent, since the child never agreed to the modification, alongside broader worries about fairness and inequality. This unresolved debate will only grow more urgent as genetic technology advances.

Two people can make the same decision, but only one faces a bad outcome due to pure chance, raising the question of whether they deserve different moral judgment. This unresolved issue challenges standard assumptions about fairness in blame and praise.