Algorithms shape high-stakes decisions across society. While promising efficiency, algorithms also raise fairness concerns. This article proposes that algorithmic fairness is best understood as a human–technology interaction problem rather than a purely technical challenge. Algorithms can reproduce human biases, amplify them through feedback loops, or create new forms of unfairness through objectives, proxies, and seemingly neutral variables. Yet they can also make decision processes more explicit, disparities more visible, and actively mitigate discrimination. Fairness therefore depends not only on statistical properties, but also on how algorithms are designed, used, and experienced by those affected by their decisions. The paper brings together insights from social justice, psychology, computer science, judgment and decision-making, and management.