Artificial intelligence increasingly participates in organizational decisions that allocate work, opportunities, resources, evaluation, and other consequential outcomes. Yet responsible artificial intelligence is often framed as a list of abstract principles or technical safeguards rather than as an organizational policy problem involving authority, review, challenge, and continuing accountability. This Original Organizational Policy Framework Article develops a proposed framework for transparent and contestable organizational decision-making. The conceptual approach integrates scholarship on responsible AI governance, algorithmic management, transparency, human–AI judgment, voice, accountability, and auditing while preserving the distinction between established findings and original synthesis. The framework begins with scope and risk classification and then couples four policy domains: transparency requirements, meaningful human review and override, contestability and redress, and accountability allocation. Monitoring and periodic review provide a recursive mechanism for reassessing policy intensity as systems, data, uses, or organizational conditions change. The central proposition is that higher-risk decisions should not trigger isolated safeguards; instead, governance protections should intensify jointly and remain connected through explicit decision rights and escalation pathways. The framework further distinguishes disclosure from intelligibility, human presence from meaningful authority, voice opportunity from consequential contestation, and technological capability from legitimate responsibility. The proposed relationships are not presented as validated causal effects or universal prescriptions. Their value lies in organizing organizational policy choices into testable propositions and observable governance responsibilities. Future research should compare alternative policy architectures, examine how they are enacted in practice, and test whether particular configurations improve decision quality, fairness, accountability, or affected-party influence under specified conditions.
