Systemic Forgiveness: Why We Forgive Systems, Not People

This paper introduces the concept of systemic forgiveness—a structural tendency to absolve systems, institutions, and algorithms from responsibility while punishing individuals. Drawing on interdisciplinary frameworks from political theory, sociology, and science and technology studies, the paper identifies three key mechanisms that enable this moral asymmetry: the complexity shield, attribution bias, and de-agentified narratives. Through case studies in finance, healthcare, algorithmic governance, and infrastructure, it demonstrates how systemic actors evade accountability via abstraction, opacity, and narrative displacement, while frontline workers become scapegoats. The genealogy of this phenomenon is traced from bureaucratic hierarchies to algorithmic impunity, revealing how moral responsibility has been reframed as technical failure. The paper concludes by proposing a framework for symmetrical accountability—introducing legal personhood for systems, auditable responsibility chains, reparative indexing, and epistemologically accountable design. Ultimately, it argues that systemic forgiveness is not an ethical oversight but a political logic that must be challenged in pursuit of structural justice.