Rising living costs, energy insecurity, widening inequality, and escalating climate impacts are fueling discussions on fairness and justice in climate policy. Yet, assumptions in global emission scenarios that determine who benefits and who bears the costs are often only made implicitly. A new IIASA-led study addresses this gap by offering a practical way to assess and design emission scenarios that explicitly account for distributive justice.

New study shows that modern AI systems don’t just process information, they systematically “judge” people in ways that resemble human trust, but with important differences. Like humans, they favor competence and integrity, yet they do so in a more rigid, rule-based, and often more extreme way. Crucially, their judgments can also be more consistently biased across demographic traits and vary significantly between models. The bottom line: AI can mimic the structure of human judgment, but it does not think like humans, and that gap matters when these systems are used to make real decisions about people.