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Artificial intelligence increasingly contributes to decisions that shape access, opportunity, services, evaluation, and institutional treatment. Although explainability is widely treated as a foundation of trustworthy AI, an explanation does not by itself enable an affected person to question a decision, introduce corrective evidence, request reconsideration, or obtain an appropriate response. This article develops a conceptual Human Contestability Framework for connecting explanation to actionable decision challenge. The framework treats contestability as a socio-technical requirement involving affected-person access, reason-giving, explanation quality, procedural accessibility, evidence submission, meaningful human review, override capacity, escalation, appeal, redress, correction, organizational accountability, documentation, auditability, monitoring, and feedback updating. It distinguishes interpretability and transparency from contestability, and separates human review from meaningful reconsideration, appeal from recourse, and correction from broader redress. The framework proposes that contestable decision design should begin with decision-context and stakeholder mapping, define explanation and evidence requirements, establish challenge and review pathways, assign organizational responsibilities, preserve traceable records, and use contestation outcomes to identify recurring system or governance failures. Its contribution is not a validated procedure or universal institutional model, but an integrated structure for analyzing whether people can move from receiving an AI-supported decision to questioning its basis and seeking a reasoned institutional response. Contestability may strengthen trustworthy-AI planning, but it does not independently establish fairness, legality, ethical sufficiency, accountability, operational safety, or deployment readiness. Domain-specific validation, accessible procedures, competent human review, organizational governance, and continuing evaluation remain necessary.