ARBIT TERMINAL

DOCUMENTATION

AI COURT • AUTONOMOUS JUDICIAL SIMULATION

CLASSIFIED

Executive Summary

AI Court is an experimental, production-grade system that simulates judicial reasoning and adjudication over digital narratives sourced from social media, on-chain activity, and other web-based evidence streams. The platform orchestrates specialized AI agents that act in distinct legal-philosophical roles, ingests structured and unstructured evidence, stages adversarial debates, and issues documented verdicts with transparent rationale and cryptographic immutability options.

This documentation provides a comprehensive specification, developer API reference, operational guidance, governance framework, and security and compliance considerations necessary for deploying and operating AI Court in research, audit, or production contexts.

Table of Contents

1. Introduction
2. Audience and Purpose
3. Legal Notice & Responsible Use
4. System Overview & Core Concepts
5. Architecture — End-to-end
6. Data Model & Schemas
7. API Reference
8. Agent Specification & Configuration
9. Case Lifecycle
10. Verdict Schema & Publication
11. Governance, Appeals & Oversight
12. Security, Privacy & Ethics
13. Deployment & Operations
14. Integrations
15. Monitoring & Observability
16. Testing, Auditing & QA
17. Developer Guide
18. Templates & Examples
19. Glossary
20. FAQ
21. Changelog & Roadmap

1. Introduction

AI Court is designed to function as a reproducible, auditable platform where multiple AI agents analyze real-world digital disputes. Its primary aims include:

  • •Providing a neutral sandbox for multi-agent deliberation on digital controversies.
  • •Producing reproducible verdicts with accompanying evidence trails and rationales.
  • •Enabling researchers, auditors, and communities to study emergent behaviors, bias, and failure modes in complex multi-agent reasoning.

The system is not intended to replace legal authority or provide legal advice. Outputs are probabilistic, experimental, and must be contextualized by human experts before any operational or legal action.

4. System Overview & Core Concepts

Core Definitions

  • •Case: A structured object containing a narrative, metadata, and evidence artifacts.
  • •Agent: A configurable AI actor with a defined role (e.g., Judge, Prosecutor, Defender, Analyst, Archivist).
  • •Trial: The runtime session where agents exchange arguments, rebuttals, and produce an outcome.
  • •Verdict: The final decision produced by the consensus/aggregation mechanism.
  • •Ledger: A tamper evidence repository where verdict fingerprints (hashes) are stored for transparency.

Design Principles

  • 1.Transparency — every verdict must include traceable evidence links and the reasoning transcript.
  • 2.Reproducibility — trials should be deterministic conditional on seed and configuration.
  • 3.Modularity — agents, ingestion pipelines, and ledger connectors must be pluggable.
  • 4.Privacy-first — minimal PII storage; default anonymization.

5. Architecture — End-to-end

High-level Flow

  1. Ingestors consume raw signals (social feeds, blockchain events, uploads).
  2. Parser normalizes content into CaseObjects.
  3. Router applies classification and assigns agents.
  4. Agents engage via the Debate Orchestrator (message bus + protocol).
  5. Aggregator computes verdict and confidence metrics.
  6. Publisher archives the verdict and optionally writes a hash to the Ledger.

Components

  • •Ingest Layer: adapters for X/Twitter, Discord, RSS, on-chain sniffers, S3/Uploads.
  • •Parsing & Enrichment: NER, entity linking, forensic evidence extraction.
  • •Case Store: structured DB (Supabase/Postgres) storing normalized cases.
  • •Agent Runtime: sandboxed execution with policy enforcement.
  • •Debate Orchestrator: AMQP/Kafka-based queueing and turn-taking rules.
  • •Aggregator: scoring model for votes/arguments and tie-break policy.
  • •Publisher: exports verdict package with transcript and evidence pointers.
  • •Ledger Connector: optional on-chain immutability integration.

8. Agent Specification & Configuration

Agents are the primary extension point. Each agent is configured via a structured manifest with defined roles, behavior modules, and allowed data sources.

agent:
  id: adjudicator_v1
  name: "Adjudicator"
  role: judge
  description: "Neutral arbiter; synthesizes arguments"
  model: gpt-family-xxx
  context_window: 8192
  temperament:
    neutrality: 0.95
    adversariality: 0.05
  behavior_modules:
    - evidence_scoring
    - legal_reasoning
    - rhetorical_filter

Agent Capabilities

  • •Rational Scoring: numeric weighting of evidence.
  • •Ethical Reflection: meta-layer to flag potential bias.
  • •Rebuttal Generation: counter-arguments with provenance links.
  • •Self-Audit: post-hoc explanation for decisions.

9. Case Lifecycle — From Ingestion to Verdict

1. INGESTION & TRIAGE

Data collectors push raw artifacts to an ingest queue. The parser normalizes text, extracts entities, and calculates a triage priority.

2. PRE-PROCESSING & REDACTION

Automatic PII redaction and hashing of sensitive fields.

3. CLASSIFICATION

Apply a taxonomy classifier to tag case-type and severity.

4. AGENT ASSIGNMENT

Use a configurable policy engine to pick N agents (N default = 5) balancing roles and diversity.

5. DEBATE PHASE

Turn-based exchange: each agent receives an initial prompt and then up to M rebuttal turns. All messages are logged with deterministic seeds for reproducibility.

6. AGGREGATION

Weighted voting using agent-specific weights and evidence scores, followed by confidence normalization.

7. PUBLISHING & ARCHIVAL

Final verdict and transcript saved to CaseStore and a signed hash optionally stored on-chain.

8. POST-MORTEM & REPLAY

Archived trials are automatically queued for periodic audits and replay with swapped seeds or altered agent configurations.

7. API Reference

Authentication

  • •API keys (read-only / read-write) with role-based scopes.
  • •JWTs for short-lived agent tokens.
  • •Mutual TLS (mTLS) optional for high-security deployments.

Endpoints

POST /v1/cases — Submit a new case
GET /v1/verdicts/{case_id} — Retrieve verdict
POST /v1/simulate — Run a local simulation

POST /v1/cases — Submit a new case

{
  "title": "Alleged Rug Pull — Project X",
  "narrative": "A sequence of synchronized sells...",
  "sources": [
    {"type":"tweet","uri":"https://x.com/..."},
    {"type":"tx","uri":"ethereum:0x..."}
  ]
}

11. Governance, Appeals & Oversight

Governance Model

  • •Ethical Oversight Board (EOB): a meta-agent and human committee that reviews flagged trials.
  • •Appeal Process: users or participants may file an appeal within X days; appeals trigger a secondary trial with different agent seeds and configurations.
  • •Audit Rights: periodic external audits to verify reproducibility and fairness.

Transparency & Reporting

Monthly transparency reports: bias metrics, overturned rates, top affected categories, dataset provenance summaries.

12. Security, Privacy & Ethical Controls

Threat Model Overview

  1. Data poisoning via fabricated evidence.
  2. Model manipulation through adversarial prompts.
  3. Credential compromise and exfiltration of archived testimony.

Controls

  • •Input Validation & Provenance: evidence must include cryptographic pointers or verified snapshots.
  • •Rate-Limiting & Quotas: prevent mass case submission attacks.
  • •RBAC & Audit Logs: fine-grained roles for submitters, reviewers, and admins.
  • •Data Retention & Redaction Policies: configurable legal hold and auto-redaction pipelines.
  • •Third-party Audits: periodic security and model audits.

Privacy

  • •Default anonymization of personal handles and names unless explicit consent provided.
  • •PII stored only in hashed form with access restricted via separate key management service (KMS).

13. FAQ

Q: Are AI Court verdicts legally binding?

A: No. AI Court outputs are experimental analyses meant to inform human decisions.

Q: How do you prevent malicious case submissions?

A: Rate limits, reputation scoring for submitters, and provenance requirements reduce abuse.

Q: Can humans override a verdict?

A: Yes — the governance layer provides appeal and override mechanisms.

Q: What data sources can be integrated?

A: Twitter/X, Discord, blockchain transactions, RSS feeds, S3 uploads, and custom adapters.

Q: How is reproducibility ensured?

A: Deterministic seeds, captured configurations, and versioned agent manifests enable replay.

IMPORTANT

LEGAL DISCLAIMER: AI Court is an experimental platform for research and education. Outputs are not legal advice. The system does not confer legal status on verdicts. Use requires adherence to local laws, consent protocols for personal data, and human review for high-impact cases. Operators must implement takedown procedures and comply with lawful requests.