<b>AI Accountability Literacy: An Integrated Framework for Agent Identity, Authorization, Auditability, Institutional Observation, and Research Provenance</b>

It examines how the emergence of agentic AI requires educational and research practices to address not only AI use and model behavior, but also agent identity, authentication, authorization, auditability, evidence, institutional context, and research provenance.</p><p dir="ltr">The framework is developed through an independent methodological crosswalk between the <b>NIST Software and AI Agent Identity and Authorization</b> research domain and the <b>Archival Analysis & Accountability Protocols (AAAP)</b>.

NIST is treated as an external technical reference; this work does not represent NIST endorsement, validation, or institutional adoption of AAAP or the proposed framework.</p><p dir="ltr">A central contribution is the transformation:</p><p dir="ltr"><b>Institutional Communication → Research Record → Persistent Publication</b></p><p dir="ltr">This model demonstrates how institutional interactions, technical frameworks, administrative responses, and other observable events can be documented as longitudinal research observations and transformed into persistent, versioned, and citable research objects.</p><p dir="ltr">The framework integrates:</p><p dir="ltr"><b>Agent Identity → Authentication → Authorization → Action → Evidence → Verification</b></p><p dir="ltr">with the longitudinal research lifecycle:</p><p dir="ltr"><b>OBSERVE → RECORD → PUBLISH → NOTIFY → ACKNOWLEDGE → RESPOND → VERIFY → LEARN</b></p><p dir="ltr">The study connects AI education with responsible AI, AI auditing, cybersecurity, digital evidence, open science, research methodology, reproducibility, citizen auditing, institutional observation, and research provenance.</p><p dir="ltr">The resulting concept of <b>AI Accountability Literacy</b> expands AI literacy from the ability to understand and use AI systems toward the ability to examine how AI-mediated actions are identified, authorized, recorded, verified, preserved, and studied over time.</p><p dir="ltr">This document is intended as a methodological and educational reference for researchers, educators, students, AI practitioners, auditors, and independent observers working at the intersection of artificial intelligence, accountability, auditability, institutional processes, and open research infrastructure.</p>