Who is the 'I' in AI? Establishing Decentralized Identity and Accountability for Autonomous Agents
Organizations: Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh · School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, New South Wales, Australia
Abstract
The rapid proliferation of autonomous AI agents in decentralized ecosystems challenges existing identity paradigms. While human identity is traditionally anchored in biological and/or self-asserted attributes and government credentials, AI agents lack a standardized definition of selfhood, creating critical gaps in trust and accountability. This paper introduces a novel framework for establishing Decentralized AI Identity rooted in Self-sovereign identity (SSI) principles. We propose a formal model that clearly distinguishes human users and AI agents, enabling distinct verification logic for each class while maintaining a unified identity calculus. The central to our contribution is the ABCD Framework, a multi-layered approach to agent identification and verification: (A) Algorithmic Fingerprint, which binds the identity to static model architectures and provenance; (B) Behavioural Fingerprint, which verifies dynamic performance metrics over time; (C) Cryptographic Identity, which anchors the agent via Decentralized Identifiers (DIDs) and (D) Delegation, which cryptographically binds agent actions to respective human accountability. By orthogonalizing these dimensions, we model how systems can enforce granular trust policies ranging from simple authentication to complex liability assignment, thereby solving the black box identity problem in the autonomous economic activities of AI agents.
Figures & tables
| Framework Layer (Feature) | Dorri et al. [ 9 ] | LOKA Protocol [ 11 ] | Huang et al. [ 17 ] | ABCD Framework (Ours) & Core Advantage |
|---|---|---|---|---|
| (C) Cryptographic Identity | Generic IDs | UAIL (DIDs & VCs) | DIDs + VCs + ANS | DID + Formal Partition: Prevents User-to-Agent impersonation via strict entity class separation. |
| (A) Algorithmic Fingerprint | Not addressed | Ethical Consensus (CEP) | Trusted Issuers (VCs) | Intrinsic Model Hash: Cryptographically binds identity directly to code, removing 3rd-party reliance. |
| (B) Behavioural Fingerprint | Fault Detection | Post-hoc Voting | Session Management | Runtime Coherence: Detects malicious model-swapping by checking runtime metrics against Layer A. |
| (D) Delegation & Liability | Task Allocation | Dispute Resolution | Global Revocation | Cryptographic Binding: Enforces time-bounded authorization tying agent actions to a human principal. |
| Layer | Attribute Set | Purpose |
|---|---|---|
| {modelHash, architectureID, fineTuneHash, cfgSnap, versionTag, issuerDID, issuerSignature} | Static algorithmic provenance | |
| {interactionPattern, ethicalProfile, decisionTraces, latency95, refusalRate, riskProfile, benchmarkScore, taskProfile} | Dynamic runtime behaviour | |
| {DID, publicKey, keyFingerprint} | Cryptographic identity | |
| {ownerDID, ownershipVC, delegationCert, delegationWindow} | Delegation & ownership |
AI Accountability
AI accountability concerns how responsibility for AI decisions and harms is assigned, evidenced, challenged, and remedied.
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