9.3 AI-enabled dispute resolution system (n-party adjudication)

Motivation

Dispute resolution is the determination of contested claims by applying agreed rules to established facts. It is a prerequisite for high-trust coordination: parties transact more freely when they know disagreements will be resolved predictably and at bounded cost.

Current dispute resolution systems exhibit a cost-quality tradeoff:

• High-quality resolution (experienced adjudicators, thorough process, reasoned decisions) costs $1,000–$50,000+ per dispute and takes weeks to months. It is economical only for disputes exceeding these thresholds.
• Streamlined resolution (simplified process, less experienced adjudicators) reduces cost but also reduces accuracy and perceived fairness. Parties may reject outcomes or avoid the system.
• Platform-based online dispute resolution (eBay, PayPal, Alibaba) handles high volumes at low cost for narrow dispute types, but is limited to transactions within those platforms and is often perceived as favoring the platform.
• No resolution remains common for disputes where resolution cost exceeds value at stake. In commercial contexts: minor contract breaches, small-value SLA violations, inter-business payment disputes under $5,000. In autonomous systems: protocol violations, resource allocation conflicts, inter-agent coordination failures.

What AI changes: AI can reduce marginal adjudication cost from hundreds or thousands of dollars to single digits, making resolution economical for disputes currently abandoned. The 2024 cost of processing a complex document set and generating a structured decision via LLM is approximately $0.10–$10 depending on volume, compared to $200–$2,000+ for equivalent human review.

The design challenge: reduce cost while maintaining the structural properties that make outcomes respected.

Trust assumptions required

For AI-enabled dispute resolution to function as a high-trust regime:

Parties must trust:

1. Rule fidelity: The system applies the agreed rules—not undisclosed criteria, training biases, or operator preferences.
2. Evidence evaluation: Submitted evidence is assessed on relevance and weight as defined by the rules; no party receives preferential treatment based on identity, resources, or relationship to the system operator.
3. Process integrity: Counterparties cannot exploit procedural mechanisms or model behaviors to gain advantage.
4. Error correctability: Incorrect outcomes can be identified and remedied through appeal or review.
5. Confidentiality: Dispute details are not disclosed beyond authorized recipients or exploited by the system operator.

The system must trust:

6. Evidence authenticity: Submitted materials are genuine—not fabricated, altered, or misleadingly excerpted.
7. Participation good faith: Parties seek resolution of genuine disputes, not procedural harassment or precedent manipulation.
8. Outcome compliance: Parties will comply with decisions or accept enforcement.

Each assumption requires structural support. Assumptions that depend on participant goodwill will be violated when violation is advantageous.

Architecture

Participants

• Disputants ($D_1, D_2, \ldots, D_n$): Parties to the dispute; $n \geq 2$.
• AI Adjudicator ($A$): Automated system evaluating evidence and rendering decisions within delegated scope. May be a single model, an ensemble, or a structured combination (see §Adjudicator Configuration).
• Human Adjudicator ($H$): Human authority for appeals, decisions outside AI scope, and legitimacy-critical determinations.
• Protocol Governance ($G$): Governs the system itself—rule amendments, adjudicator certification, precedent curation, operator oversight.
• Enforcement Mechanism ($E$): Executes outcomes—fund transfer, access modification, state change, reputation update.

Adjudicator configuration

Parties may, at dispute initiation, select from available adjudicator configurations. The goal is dispassionate application of rules—achieved through different structural means:

    adjudicator_config:
  
  single_model:
    description: "One AI model renders decision"
    model: <model_id, version_hash, attestation>
    appropriate_for: "routine disputes, low stakes, high volume"
    weakness: "single point of failure; model-specific biases undetected"
    
  dual_model_consensus:
    description: "Two independently-operated AI models must agree"
    models: [<model_1>, <model_2>]
    consensus_rule: "unanimous" | "either_may_escalate"
    on_disagreement: "escalate_to_human" | "third_model_tiebreaker"
    appropriate_for: "moderate stakes; bias detection via disagreement"
    weakness: "correlated errors if models share training data or architecture"
    
  panel_selection:
    description: "Random selection of k models from pre-agreed list of n"
    model_pool: [<model_1>, ..., <model_n>]
    selection: 
      k: 3  # number selected
      method: "verifiable_random_selection"  # e.g., commit-reveal, VRF
    decision_rule: "majority" | "unanimous" | "supermajority"
    appropriate_for: "higher stakes; resistance to single-model gaming"
    weakness: "increased cost; potential for inconsistent reasoning"
    
  adversarial_pairing:
    description: "Each party selects one AI; neutral AI or human resolves disagreement"
    party_models: {D_1: <model_a>, D_2: <model_b>}
    resolver: <neutral_model> | <human_adjudicator>
    appropriate_for: "parties distrust single adjudicator; want advocacy representation"
    weakness: "models may be selected for favorable bias; resolver bears full decision weight"
    
  human_ai_hybrid:
    description: "AI prepares analysis; human renders decision"
    ai_role: "evidence_summary" | "draft_decision" | "issue_identification"
    human_role: "final_determination"
    appropriate_for: "high stakes; novel issues; legitimacy-critical"
    weakness: "higher cost; human availability constraints"
    
  sequential_review:
    description: "AI decides; second AI reviews; disagreement escalates"
    primary: <model_1>
    reviewer: <model_2>
    reviewer_scope: "error_check" | "de_novo_review"
    on_disagreement: "escalate_to_human" | "detailed_reasoning_required"
    appropriate_for: "balance of efficiency and error-catching"
    weakness: "reviewer may defer to primary; correlated errors"

  

Dispassion guarantees:

Different configurations provide dispassion through different mechanisms:

Model diversity requirements:

For multi-model configurations, diversity is necessary to prevent correlated failure:

    diversity_requirements:
  training_data: "non_overlapping" | "partially_overlapping" | "unknown"
  architecture: "different_families" | "same_family_different_versions" | "identical"
  operator: "different_organizations" | "same_organization"
  attestation: "independent_certifiers" | "same_certifier"
  
  minimum_for_bias_resistance:
    dual_consensus: "different operators OR different architectures"
    panel_selection: "at least 2 different operators among k selected"

  

Dispute lifecycle

    dispute_lifecycle:
  
  1_initiation:
    initiator: <disputant_id>
    respondents: [<disputant_ids>]
    claim: 
      type: <dispute_category>
      facts_alleged: <structured_statement>
      rule_violations_claimed: [<rule_ids>]
      remedy_requested: <specific_outcome>
    rule_set: <rule_set_id, version>
    adjudicator_config: <selected_configuration>
    evidence_initial: <submission>
    filing_fee: <amount, paid_to>
    escrow: <if_required_for_enforcement>
    
  2_response:
    deadline: <timestamp, e.g., 14_days_from_notice>
    for_each_respondent:
      response_type: "accept_liability" | "contest" | "counterclaim" | "jurisdictional_objection"
      facts_disputed: [<fact_ids>]
      defenses: [<defense_types>]
      evidence: <submission>
      counterclaim: <if_applicable, same_structure_as_claim>
      
  3_evidence_phase:
    rounds: <max, e.g., 3>
    per_round:
      deadline: <timestamp>
      submissions: [<per_party>]
      challenges: [<to_opposing_evidence>]
      responses_to_challenges: [<per_party>]
    evidence_close: <timestamp>
    late_submission: "excluded" | "admitted_with_explanation_for_delay"
    
  4_evaluation:
    adjudicator: <per_adjudicator_config>
    inputs: 
      rule_set: <frozen_at_initiation>
      evidence: <all_admitted_evidence>
      submissions: <all_party_submissions>
      precedent: <relevant_prior_decisions, retrieved>
    process:
      fact_finding: <determine_disputed_facts>
      rule_application: <apply_rules_to_found_facts>
      remedy_determination: <select_appropriate_remedy>
    outputs:
      determination: <structured_outcome>
      reasoning: <per_reasoning_requirements>
      confidence: <per_fact, per_conclusion>
      
  5_decision:
    issued: <timestamp>
    content:
      findings_of_fact: [<structured>]
      conclusions_of_rule: [<structured>]
      outcome: <determination>
      remedy: <specific_ordered_actions>
      costs: <allocation>
    reasoning: <full_explanation>
    appeal_window: <duration, e.g., 30_days>
    
  6_appeal:
    if_invoked:
      grounds: <specific_error_claimed>
      filing: <deadline, fee>
      review_scope: <per_grounds>
      reviewer: <per_adjudicator_config_appeal_tier>
      outcome: "affirm" | "reverse" | "modify" | "remand"
      
  7_enforcement:
    trigger: "appeal_window_expired" | "appeal_exhausted" | "parties_waive_appeal"
    mechanism: <designated_at_initiation>
    execution: <outcome_effectuation>
    confirmation: <verification_of_execution>
    
  8_closure:
    record:
      retention: <duration>
      access: <authorized_parties>
    precedent:
      extraction: <if_meets_criteria>
      anonymization: <per_protocol>
      publication: <per_protocol>

  

Rule specification

Dispute resolution requires rules. AI adjudication requires rules precise enough for machine application.

    rule_set:
  metadata:
    identifier: <rule_set_id>
    version: <semantic_version>
    effective_date: <timestamp>
    supersedes: <prior_version, if_any>
    
  scope:
    dispute_types: [<categories_covered>]
    party_eligibility: [<requirements>]
    transaction_types: [<if_applicable>]
    exclusions: [<explicit_carve_outs>]
    
  definitions:
    terms: 
      <term>: 
        definition: <precise_statement>
        examples: [<clarifying_instances>]
        counter_examples: [<what_is_not_included>]
    evidence_types:
      <type>:
        format: <specification>
        admissibility: <criteria>
        weight_factors: [<what_affects_probative_value>]
        
  substantive_rules:
    - rule_id: <unique_id>
      name: <human_readable>
      condition:
        predicate: <logical_expression_over_facts>
        required_evidence: <what_must_be_shown>
        burden: <which_party_must_prove>
        standard: "preponderance" | "clear_and_convincing" | "beyond_reasonable_doubt"
      consequence:
        if_satisfied: <outcome>
        if_not_satisfied: <outcome>
      priority: <for_conflict_with_other_rules>
      exceptions: [<conditions_where_rule_does_not_apply>]
      
  procedural_rules:
    deadlines:
      response: <duration_from_notice>
      evidence_round: <duration>
      calculation: <business_days | calendar_days, timezone>
    extensions:
      grounds: [<acceptable_reasons>]
      limit: <maximum_extension>
    defaults:
      on_no_response: <consequence>
      on_no_evidence: <consequence>
      
  remedies:
    available:
      - remedy_type: <type>
        description: <what_it_entails>
        prerequisites: [<conditions_for_availability>]
        limits: <caps_or_constraints>
    selection_criteria: <how_adjudicator_chooses_among_available>
    default: <when_no_specific_remedy_fits>
    
  interpretation:
    principles: [<canons_of_construction>]
    ambiguity_resolution: <method>
    gap_filling: <what_happens_when_rules_silent>
    ai_limitations:
      escalate_if: [<conditions_requiring_human_judgment>]
      
  governance:
    amendment:
      process: <how_rules_change>
      notice: <advance_notice_required>
      version_control: <how_versions_are_tracked>
    disputes_about_rules: <meta_resolution_process>

  

AI applicability constraints:

Rules suitable for AI adjudication satisfy:

1. Decidability: Given admissible evidence, the rule yields a determinate outcome or explicit indeterminacy (triggering escalation).
2. Evidence mapping: The rule specifies what evidence is relevant and how evidence relates to the condition.
3. Composability: When multiple rules apply, their interaction is defined.
4. Boundary specification: The rule's scope is explicit; gaps are acknowledged, not papered over.

Rules containing undefined terms like "reasonable," "appropriate," or "fair" without further specification are flagged for human adjudication unless the rule set provides operational definitions.

AI adjudicator specification

    ai_adjudicator:
  identity:
    model_id: <identifier>
    version: <hash_of_weights_and_inference_config>
    attestation: 
      certifier: <certifying_body>
      scope: <what_was_certified>
      date: <certification_date>
      expiration: <if_applicable>
    operator: <organization_running_the_model>
    
  capabilities:
    evidence_types: [<formats_model_can_process>]
    evidence_size_limit: <maximum_per_submission>
    total_context_limit: <maximum_per_dispute>
    party_limit: <maximum_n>
    rule_set_compatibility: [<rule_set_ids_model_is_certified_for>]
    
  constraints:
    decision_scope:
      permitted: [<outcome_types_model_may_render>]
      reserved_for_human: [<outcome_types_requiring_escalation>]
      stakes_limit: <maximum_value_at_stake>
    confidence_requirements:
      fact_finding_threshold: 0.7  # below this, fact is "not established"
      decision_threshold: 0.8  # below this, escalate to human
      calibration_method: <how_confidence_is_computed>
      calibration_validation: <how_calibration_is_verified>
    reasoning_requirements:
      must_cite: ["specific_evidence", "specific_rules", "inference_steps"]
      must_not: ["external_knowledge", "party_identity_factors", "unstated_premises"]
      
  inference_environment:
    determinism: 
      level: "reproducible_given_fixed_seed_and_environment"
      seed_disclosure: "included_in_decision_record"
      hardware_specification: <for_reproducibility>
    isolation: "no_network_access_during_inference" | "allowlisted_endpoints_only"
    input_commitment: <hash_of_all_inputs_recorded_before_inference>
    output_signature: <decision_cryptographically_signed_by_operator>
    
  monitoring:
    decision_log: 
      contents: [input_hash, output_hash, timestamp, seed, confidence_scores]
      integrity: "append_only_hash_chain"
      retention: <duration>
    outcome_tracking:
      by_rule: <appeal_rate, reversal_rate_per_rule>
      by_party_characteristic: <for_bias_detection, see_bias_monitoring>
      by_dispute_type: <for_quality_assessment>
    anomaly_detection:
      patterns_monitored: [outcome_distribution_shift, confidence_distribution_shift, processing_time_anomalies]
      alert_threshold: <statistical_threshold>
      
  bias_monitoring:
    protected_characteristics: [<defined_by_protocol_governance>]
    metrics:
      outcome_parity: <difference_in_win_rates>
      calibration_parity: <difference_in_confidence_accuracy>
      appeal_parity: <difference_in_appeal_rates>
    base_rates: <expected_distribution_given_case_mix>
    detection_method: <statistical_test>
    threshold_for_review: <significance_level>
    response_to_detected_bias: "human_audit" | "model_suspension" | "recertification_required"

  

Confidence calibration:

Confidence scores are useful only if calibrated—i.e., when the model reports 80% confidence, it is correct approximately 80% of the time.

    calibration:
  method: 
    training: "temperature_scaling" | "platt_scaling" | "isotonic_regression"
    validation: "held_out_test_set" | "ongoing_appeal_outcomes"
  validation_data:
    source: "historical_disputes_with_known_outcomes"
    size: <minimum_sample>
    recency: <must_include_recent_disputes>
  recalibration_trigger:
    schedule: "quarterly" | "after_N_decisions"
    performance_threshold: "ECE > 0.1"  # expected calibration error
  limitation_acknowledgment: >
    Confidence calibration is an active research area. Current methods may not 
    generalize to novel dispute types or adversarial inputs. Confidence scores 
    should be interpreted as approximate, not precise probabilities.

  

Evidence protocol

    evidence_protocol:
  submission:
    format:
      documents: [pdf, docx, txt, md]
      structured_data: [json, csv, xml]
      images: [png, jpg, pdf]
      multimedia: [mp4, mp3] # if rule set permits
      size_limit_per_item: <e.g., 50MB>
      total_limit_per_party: <e.g., 500MB>
    authentication:
      required_level: "cryptographic_signature" | "sworn_attestation" | "self_declaration"
      signature_verification: <method>
      attestation_liability: <consequences_of_false_attestation>
    metadata:
      required: [submission_timestamp, submitting_party, evidence_description, relevance_claim]
      
  chain_of_custody:
    for_digital_evidence:
      hash_at_submission: <recorded>
      source_attestation: <origin_of_evidence>
      modification_history: <if_applicable>
    for_physical_evidence:
      not_supported: "digital_submissions_only" | "require_certified_digitization"
      
  admissibility:
    type_check: <evidence_type_defined_in_rule_set>
    relevance:
      standard: "tends_to_prove_or_disprove_disputed_fact"
      determination: "by_adjudicator" | "by_rule_mapping"
    authenticity:
      presumption: "authentic_unless_challenged"
      challenge_procedure: <see_challenges>
    exclusions:
      privileged: [<privilege_types_recognized>]
      prejudicial: <if_rule_set_includes_such_exclusions>
      procedural: "untimely_submission" | "exceeds_limits"
      
  challenges:
    types: ["authenticity", "relevance", "admissibility", "weight"]
    procedure:
      challenger_submission: <specific_grounds>
      response_deadline: <timestamp>
      ruling: "by_adjudicator" | "preliminary_by_ai_final_by_human"
    consequences:
      sustained: "evidence_excluded" | "evidence_admitted_with_reduced_weight"
      overruled: "evidence_admitted" | "challenge_noted_for_weight"
      
  weighting:
    factors:
      source_type: 
        contemporaneous_record: "higher_weight"
        after_the_fact_statement: "lower_weight"
        third_party_neutral: "higher_weight"
        party_generated: "lower_weight"
      corroboration:
        multiple_independent_sources: "increased_weight"
        single_source: "baseline_weight"
      specificity:
        detailed_and_specific: "higher_weight"
        vague_or_general: "lower_weight"
    application: "by_adjudicator_per_rule_set_guidance"
    
  fraud_deterrence:
    detection:
      cross_reference: <checking_consistency_across_submissions>
      metadata_analysis: <detecting_manipulation_artifacts>
      statistical_anomaly: <unusual_patterns_in_submission>
      external_verification: <where_possible, e.g., public_records>
    penalties:
      for_fabrication: "case_dismissal" | "adverse_judgment" | "system_exclusion" | "referral_to_authorities"
      for_material_omission: "adverse_inference" | "reduced_credibility"
      for_misleading_presentation: "adverse_inference" | "cost_sanctions"
    limitation: >
      Evidence fraud detection is imperfect. Sophisticated fabrication may escape 
      detection. Penalties apply when fraud is discovered; undetected fraud 
      remains a system vulnerability.

  

Reasoning and explanation

Decisions must be explicable. Unexplained outcomes cannot be evaluated, appealed, or trusted.

    reasoning_specification:
  structure:
    findings_of_fact:
      for_each_disputed_fact:
        fact_id: <id>
        description: <what_was_alleged>
        finding: "established" | "not_established" | "undetermined"
        evidence_basis:
          supporting: [<evidence_citations_with_quotations_or_descriptions>]
          opposing: [<evidence_citations>]
          resolution: <why_supporting_outweighs_opposing_or_vice_versa>
        confidence: <score_with_interpretation>
        
    rule_application:
      for_each_applicable_rule:
        rule_id: <id>
        condition_analysis:
          elements: [<condition_components>]
          per_element:
            satisfied: true | false
            by_facts: [<fact_ids_establishing_element>]
        conclusion: "rule_triggered" | "rule_not_triggered"
        
    outcome_derivation:
      from_triggered_rules: [<rule_ids>]
      conflicts_resolved: <if_multiple_rules, how_resolved>
      remedy_selection:
        available_remedies: [<per_triggered_rules>]
        selected: <remedy>
        rationale: <why_this_remedy>
        
    precedent_consideration:
      relevant_precedents: [<precedent_ids>]
      application: <how_precedents_informed_decision>
      distinctions: <if_departing_from_precedent, why>
      
  requirements:
    completeness:
      every_disputed_fact: "must_have_finding_with_evidence_basis"
      every_finding: "must_cite_specific_evidence"
      every_rule_application: "must_cite_specific_facts"
      every_conclusion: "must_follow_from_stated_premises"
    prohibitions:
      no_unstated_premises: true
      no_external_knowledge: true  # only evidence in record
      no_party_identity_factors: true  # unless legally relevant
      no_speculation: true  # only established facts
    transparency:
      confidence_disclosure: "required_for_each_material_finding"
      uncertainty_acknowledgment: "required_where_present"
      alternative_interpretations: "noted_where_reasonable"
      
  format:
    human_readable:
      summary: <plain_language_overview, 1-2_paragraphs>
      full_reasoning: <complete_structured_explanation>
    machine_readable:
      schema: <json_schema_for_structured_reasoning>
      linkage: <evidence_ids_linked_to_finding_ids_linked_to_rule_ids>
    verification:
      trace_available: "full_inference_trace_on_request"
      reproducibility: "re_running_adjudicator_on_same_inputs_yields_same_output"

  

Precedent system

Prior decisions inform future disputes. Precedent improves consistency and predictability but creates risks around confidentiality and manipulation.

    precedent:
  extraction:
    criteria:
      novelty: "interprets_rule_in_new_context"
      significance: "affects_future_disputes"
      quality: "reasoning_meets_publication_standard"
    exclusions:
      default_judgments: "excluded"
      settled_before_decision: "excluded"
      party_requests_exclusion: <per_protocol_rules>
    extractor: "human_review" | "ai_extraction_with_human_approval"
    
  anonymization:
    required_removals:
      party_identities: "replaced_with_generic_labels"
      identifying_details: "generalized_or_redacted"
      confidential_business_information: "redacted"
    re_identification_risk:
      assessment: "required_before_publication"
      threshold: "publication_blocked_if_risk_exceeds_X"
      mitigation: "additional_generalization" | "delayed_publication" | "restricted_access"
    limitation: >
      Anonymization reduces but does not eliminate re-identification risk. 
      Parties with knowledge of specific disputes may recognize them despite 
      anonymization. Highly distinctive fact patterns may be identifiable.
      
  storage:
    format: "structured_searchable_database"
    fields: [fact_pattern_abstract, rules_applied, holdings, reasoning_summary, date, adjudicator_config]
    access:
      ai_adjudicators: "full_access_for_retrieval"
      parties: "search_access" | "restricted"
      public: "published_subset" | "none"
      
  application:
    retrieval:
      method: "semantic_similarity" | "rule_based_matching" | "hybrid"
      relevance_threshold: <minimum_similarity_for_inclusion>
      max_precedents: <limit_to_prevent_context_overflow>
    weight:
      binding_vs_persuasive: <defined_by_rule_set>
      recency: "recent_precedents_weighted_higher"
      adjudicator_tier: "human_decisions_weight_higher_than_ai_only"
    distinguishing:
      requirement: "if_departing_from_relevant_precedent, must_explain"
      valid_grounds: [factual_distinction, rule_change, prior_error]
      
  evolution:
    correction:
      process: <for_precedent_later_deemed_erroneous>
      effect: "prospective_only" | "retroactive_review_available"
    obsolescence:
      trigger: "rule_change" | "superseding_decision" | "time_limit"
      marking: "obsolete_precedents_flagged_in_retrieval"
    manipulation_resistance:
      collusion_detection:
        method: "pattern_analysis_of_filings_and_outcomes"
        limitation: "sophisticated_collusion_difficult_to_detect"
      review: "precedent_setting_decisions_subject_to_human_review"

  

Appeal mechanism

    appeal:
  availability:
    right: "automatic" | "discretionary" | "by_leave"
    fee: <amount>
    fee_allocation: "refunded_if_appeal_succeeds" | "non_refundable"
    
  grounds:
    permitted:
      procedural_error:
        definition: "process_rules_were_not_followed"
        examples: [missed_deadline_not_enforced, evidence_improperly_excluded, wrong_adjudicator_config]
      evidence_error:
        definition: "evidence_was_materially_misread_or_ignored"
        examples: [clear_evidence_not_cited, evidence_misconstrued, weight_grossly_inappropriate]
      rule_error:
        definition: "rule_was_misapplied_or_misinterpreted"
        examples: [wrong_rule_applied, rule_condition_misread, exception_ignored]
      reasoning_error:
        definition: "conclusion_does_not_follow_from_stated_premises"
        examples: [logical_gap, contradiction, unsupported_inference]
      new_evidence:
        definition: "material_evidence_unavailable_at_original_hearing"
        requirements: [explain_unavailability, show_materiality]
    prohibited:
      mere_disagreement: "must_identify_specific_error"
      weight_of_evidence: "unless_grossly_unreasonable"
      credibility_determinations: "unless_no_reasonable_basis"
      
  filing:
    deadline: <e.g., 30_days_from_decision>
    requirements:
      specific_error_identification: "required"
      supporting_argument: "required"
      proposed_outcome: "required"
    deficient_filing: "rejected_with_opportunity_to_cure" | "rejected_final"
    
  process:
    respondent_reply:
      deadline: <e.g., 21_days_from_appeal_filing>
      content: "response_to_claimed_errors"
    reviewer:
      first_appeal:
        from_single_ai: "different_ai_or_human"
        from_ai_panel: "human_reviewer"
        from_human: "appellate_human_panel"
      second_appeal: "if_permitted, human_panel"
    review_scope:
      procedural_error: "de_novo"
      evidence_error: "abuse_of_discretion"
      rule_error: "de_novo"
      reasoning_error: "de_novo"
      new_evidence: "remand_for_consideration"
    timeline: <e.g., decision_within_60_days>
    
  outcomes:
    affirm: "original_decision_stands"
    reverse: "opposite_outcome"
    modify: "change_remedy_only"
    remand: "return_for_reconsideration_with_instructions"
    
  finality:
    exhaustion: "decision_final_after_appeals_exhausted_or_waived"
    enforcement_trigger: <upon_finality>
    
  escalation_triggers_automatic:
    - ai_confidence_below_threshold
    - stakes_exceed_ai_authority
    - novel_fact_pattern_detected_by_retrieval_failure
    - rule_gap_identified
    - party_invokes_human_review_right
    - outcome_would_create_significant_precedent
    - adversarial_pattern_suspected

  

Enforcement integration

Resolution without enforcement is advisory. Effective enforcement requires integration with mechanisms that can effectuate outcomes.

    enforcement:
  mechanism_types:
    financial:
      escrow:
        requirement: "parties_deposit_before_dispute_or_at_initiation"
        release: "to_prevailing_party_per_decision"
        shortfall: "judgment_recorded; collection_outside_system"
      payment_instruction:
        integration: <payment_processor_api>
        authorization: "pre_authorized_at_system_enrollment" | "per_dispute"
        failure: "recorded; alternative_enforcement"
      bond_claim:
        against: "performance_bond_posted_at_enrollment"
        process: <bond_terms>
        
    access_control:
      permission_modification:
        scope: "within_integrated_systems"
        types: [revocation, attenuation, suspension]
        reversibility: "if_decision_overturned"
      platform_standing:
        consequences: [reduced_privileges, increased_scrutiny, exclusion]
        
    reputation:
      outcome_publication:
        to: "system_reputation_registry"
        content: "outcome_only" | "outcome_and_reasoning_summary"
        party_consent: "required" | "waived_at_enrollment"
      compliance_record:
        tracking: "decision_compliance_status"
        consequences: "non_compliance_affects_future_disputes"
        
    state_modification:
      smart_contract:
        if_applicable: "decision_triggers_contract_state_change"
        irreversibility: <per_contract_design>
      registry_update:
        authoritative_registries: [<integrated_registries>]
        
    external_referral:
      to_courts:
        when: "enforcement_requires_legal_authority"
        facilitation: "system_provides_decision_record"
      to_regulators:
        when: "violations_implicate_regulatory_concerns"
        
  pre_commitment:
    at_enrollment:
      parties_designate: "available_enforcement_mechanisms"
      parties_authorize: "execution_of_outcomes_via_designated_mechanisms"
    at_dispute_initiation:
      confirm_or_modify: "enforcement_designation"
      escrow_requirement: <if_applicable>
      
  execution:
    trigger: "decision_final"
    automation_level:
      full: "execution_without_human_confirmation"
      confirmed: "human_confirms_before_execution"
      manual: "human_executes"
    timeout: <deadline_for_execution>
    failure_handling:
      retry: <attempts>
      alternative: <fallback_mechanism>
      recording: "non_execution_recorded; affects_party_standing"
      
  reversal:
    if_decision_overturned:
      financial: "reverse_transfer_if_possible"
      access: "restore_permissions"
      reputation: "update_record"
      state: "depends_on_reversibility_of_original_change"
      
  limitations:
    jurisdictional: >
      Enforcement mechanisms operate within integrated systems. Cross-border 
      enforcement, enforcement against non-integrated assets, and enforcement 
      requiring legal compulsion depend on external legal systems and may not 
      be achievable through this protocol.
    practical: >
      Enforcement against judgment-proof parties or parties who exit the 
      ecosystem may be ineffective. System design assumes parties have 
      stake in continued participation.

  

Cost allocation

    cost_allocation:
  fee_types:
    filing_fee:
      amount: <fixed_or_scaled_to_claim_value>
      paid_by: "initiating_party"
      purpose: "deter_frivolous_filings; fund_system"
    response_fee:
      amount: <if_applicable>
      paid_by: "responding_party"
    adjudication_fee:
      amount: <based_on_adjudicator_config_and_complexity>
      initial_allocation: "split_equally" | "paid_by_initiator"
    appeal_fee:
      amount: <higher_than_filing_fee>
      paid_by: "appellant"
      
  final_allocation:
    default_rule: "loser_pays" | "each_pays_own" | "split" | "proportional_to_outcome"
    adjudicator_discretion: "may_adjust_for_party_conduct"
    factors:
      frivolous_claims: "claimant_bears_all"
      frivolous_defenses: "respondent_bears_all"
      mixed_outcome: "proportional_allocation"
      procedural_abuse: "abusing_party_bears_costs"
      
  fee_waiver:
    availability: <if_system_provides>
    criteria: <financial_hardship; meritorious_claim>
    
  transparency:
    fee_schedule: "published"
    estimated_cost: "provided_at_initiation"

  

Multi-party disputes

For disputes involving $n > 2$ parties:

    multiparty:
  configurations:
    bilateral_with_third_party_interest:
      structure: "primary_dispute_between_two; third_party_affected"
      third_party_role: "intervenor" | "amicus"
    joint_claimants:
      structure: "multiple_parties_with_aligned_claims_against_respondent(s)"
      representation: "joint_or_separate"
      outcome: "joint_judgment_or_severed"
    joint_respondents:
      structure: "claimant_against_multiple_respondents"
      liability: "joint_and_several" | "several_only" | "allocated"
    cross_claims:
      structure: "respondents_have_claims_against_each_other"
      procedure: "consolidated_with_main_dispute"
    multi_polar:
      structure: "multiple_parties_with_non_aligned_interests"
      complexity: "highest"
      
  procedural_adaptations:
    evidence_visibility:
      default: "all_parties_see_all_evidence"
      alternatives:
        bilateral_confidential: "evidence_visible_only_to_submitter_and_adjudicator"
        need_to_know: "evidence_visible_to_parties_affected_by_claims_it_supports"
    response_sequencing:
      simultaneous: "all_parties_respond_by_same_deadline"
      sequential: "ordered_response_schedule"
    consolidated_vs_severed:
      criteria: "related_claims_heard_together_unless_prejudicial"
      determination: "by_adjudicator_at_case_management_phase"
      
  alignment_determination:
    method: "self_declaration" | "adjudicator_determination"
    disputes_about_alignment: "resolved_by_adjudicator"
    consequences: "aligned_parties_may_share_costs_and_representation"
    
  outcome_structure:
    per_party_findings: "findings_specific_to_each_party"
    joint_findings: "findings_applicable_to_multiple_parties"
    liability_allocation:
      among_co_liable: "percentage_allocation"
      joint_and_several: "any_party_liable_for_full_amount"
    remedy_allocation: "specific_to_each_party_or_shared"
    contribution:
      among_co_liable: "party_who_pays_more_may_seek_contribution"
      enforcement: "through_separate_dispute_or_integrated"

  

Bootstrapping

The system requires initial rules, certified adjudicators, and participant trust—none of which exist at launch.

    bootstrap:
  rule_set_creation:
    initial_drafting:
      by: "protocol_governance" | "founding_participants" | "external_expert_body"
      process: "drafting → comment → revision → adoption"
      legitimacy_source: "expertise" | "participant_consent" | "regulatory_endorsement"
    minimum_viable_rules:
      scope: "limited_dispute_types_initially"
      expansion: "add_rules_as_precedent_accumulates"
      
  adjudicator_certification:
    initial_certifiers:
      problem: "who_certifies_the_first_certifiers"
      solutions:
        external_authority: "regulatory_body_or_recognized_standards_org"
        founding_consortium: "initial_participants_jointly_certify"
        reputation_import: "certifiers_with_reputation_from_other_domains"
    initial_ai_models:
      certification_basis: "performance_on_synthetic_disputes" | "performance_in_other_adjudication_contexts"
      limitations: "no_in_system_track_record; higher_escalation_rates_initially"
      
  participant_trust:
    early_participants:
      incentives: "lower_fees" | "influence_on_rules" | "first_mover_advantage"
      risk_tolerance: "higher_than_later_participants"
    trust_building:
      transparency: "publish_all_non_confidential_decisions_and_outcomes"
      track_record: "accumulate_appeal_rates_reversal_rates_compliance_rates"
      external_validation: "third_party_audits_of_system_performance"
      
  graduated_scope:
    initial: 
      stakes_limit: <low, e.g., $10,000>
      dispute_types: <narrow>
      adjudicator_config: "human_ai_hybrid_only"
    expansion_triggers:
      track_record: "N_disputes_resolved_with_<X%_reversal_rate"
      participant_growth: "M_active_participants"
      stability: "no_major_failures_for_T_months"
    expansion_steps:
      increase_stakes_limit: <progressive>
      add_dispute_types: <based_on_demand_and_rule_development>
      enable_ai_only_adjudication: <for_qualifying_disputes>

  

Governance

    governance:
  protocol_authority:
    composition:
      options:
        nonprofit_entity: "dedicated_governance_organization"
        consortium: "representatives_of_participant_classes"
        dao: "token_weighted_or_reputation_weighted_voting"
        hybrid: "combination"
      requirements:
        diversity: "no_single_stakeholder_class_dominates"
        expertise: "includes_technical_legal_and_domain_expertise"
        accountability: "removal_mechanism_for_underperformance"
        
    responsibilities:
      rule_management: "propose, review, adopt rule changes"
      adjudicator_certification: "certify_and_decertify_adjudicators"
      precedent_curation: "review_precedent_quality_and_consistency"
      system_monitoring: "oversee_performance_metrics_and_bias_monitoring"
      dispute_about_system: "resolve_meta_disputes"
      
    constraints:
      no_individual_case_intervention: "governance_does_not_decide_disputes"
      prospective_changes_only: "rule_changes_apply_to_future_disputes"
      notice_requirements: "changes_announced_in_advance"
      
  rule_amendment:
    proposal:
      who_may_propose: "governance_body" | "participant_petition"
      requirements: [rationale, draft_language, impact_assessment]
    review:
      comment_period: <duration>
      revision: "based_on_comments"
    adoption:
      voting: <per_governance_structure>
      threshold: <majority, supermajority, consensus>
    effective_date:
      notice_period: <minimum_advance_notice>
      pending_disputes: "old_rules_apply"
      
  adjudicator_oversight:
    performance_review:
      metrics: [appeal_rate, reversal_rate, confidence_calibration, bias_metrics]
      frequency: <quarterly, annually>
      threshold: <performance_standards>
    certification_renewal:
      required: <annually, on_material_change>
      process: <re_evaluation_against_standards>
    decertification:
      grounds: [performance_below_threshold, bias_detected, integrity_breach]
      process: [notice, opportunity_to_respond, governance_decision]
      effect: "adjudicator_removed_from_available_pool"
      
  transparency:
    published:
      rules: "current_and_historical_versions"
      fee_schedule: "current"
      adjudicator_roster: "with_performance_metrics"
      system_statistics: [volume, outcomes_by_type, appeal_rates, reversal_rates]
      governance_decisions: "meeting_minutes_and_decisions"
    audit_rights:
      participants: "may_request_audit_of_own_disputes"
      external: "periodic_independent_audit_of_system"
      
  temporal_consistency:
    rule_version:
      applicable: "version_in_effect_at_dispute_initiation"
      exceptions: "procedural_rules_may_update_mid_dispute_if_not_prejudicial"
    adjudicator_version:
      applicable: "version_certified_at_adjudicator_selection"
      mid_dispute_updates: "not_applied_to_pending_disputes"

  

Adversarial robustness

Parties will attempt to exploit the system. Design must anticipate strategic behavior.

Attack vectors and mitigations:

Structural defenses:

    adversarial_defense:
  input_controls:
    schema_enforcement: "reject_malformed_submissions"
    size_limits: "prevent_resource_exhaustion"
    rate_limits: 
      per_party: <max_disputes_per_period>
      escalating: "limits_tighten_after_frivolous_findings"
      
  behavioral_monitoring:
    filing_patterns:
      metrics: [frequency, timing, outcomes, counterparty_patterns]
      anomaly_detection: <statistical_thresholds>
    outcome_patterns:
      by_party: "unusual_win_loss_patterns"
      by_counterparty_pair: "repeated_disputes_between_same_parties"
      by_claim_type: "claim_type_shifts_suggesting_gaming"
    cross_dispute_correlation:
      collusion_indicators: [coordinated_timing, complementary_claims, mutual_benefit_patterns]
      
  deterrence:
    frivolity_penalties:
      determination: "by_adjudicator_at_any_stage"
      consequences: [cost_shifting, fee_increase, rate_limit_reduction]
    fraud_penalties:
      determination: "by_adjudicator_or_governance"
      consequences: [adverse_judgment, system_exclusion, external_referral]
    repeat_offender:
      tracking: "across_disputes"
      escalation: "progressive_penalties"
      
  model_protection:
    input_sanitization: "prevent_injection_attacks"
    inference_isolation: "no_network_access_during_inference"
    version_management:
      rotation: "periodic_model_updates"
      notice: "updates_announced; pending_disputes_use_old_version"
    multi_model:
      when: "high_stakes_or_sensitive_disputes"
      diversity: "different_training_data_or_architectures"
      disagreement_handling: "escalate_or_tiebreaker"

  

Legitimacy maintenance

Efficiency without acceptance produces decisions that are ignored. Legitimacy requires ongoing attention.

Legitimacy sources and mechanisms:

    legitimacy_monitoring:
  metrics:
    participation:
      enrollment_rate: <new_participants_per_period>
      retention_rate: <continued_participation>
      exit_rate: <departures_per_period>
      exit_reasons: <surveyed_or_inferred>
    satisfaction:
      post_dispute_survey: <winner_and_loser_separately>
      net_promoter: <would_recommend>
      complaint_rate: <formal_complaints_per_dispute>
    compliance:
      voluntary_compliance_rate: <decisions_complied_without_enforcement>
      enforcement_required_rate: <decisions_requiring_enforcement>
      non_compliance_rate: <decisions_not_complied_despite_enforcement>
    external:
      media_coverage: <sentiment_analysis>
      regulatory_attention: <inquiries_or_actions>
      academic_assessment: <independent_evaluations>
      
  thresholds:
    warning: <metric_levels_triggering_review>
    intervention: <metric_levels_triggering_action>
    
  responses:
    to_declining_participation: "fee_reduction; rule_simplification; outreach"
    to_low_satisfaction: "process_review; reasoning_quality_audit; feedback_incorporation"
    to_low_compliance: "enforcement_mechanism_review; legitimacy_investigation"
    to_external_criticism: "transparent_response; independent_audit"
    
  escalation:
    if_legitimacy_metrics_below_threshold:
      options:
        pause_ai_only_adjudication: "revert_to_human_ai_hybrid"
        reduce_scope: "lower_stakes_limits; narrow_dispute_types"
        governance_review: "comprehensive_system_assessment"
        stakeholder_consultation: "gather_input_for_redesign"

  

Failure modes and recovery

Limitations and scope

This system is appropriate for:

• Disputes where parties have pre-existing relationship or shared context (contracts, platform terms, protocol rules)
• Disputes where evidence is primarily documentary or digital
• Disputes where remedies can be executed through integrated mechanisms
• Repeated interactions where reputation matters
• Stakes where resolution cost under this system is less than stakes

This system is not appropriate for:

• Single-shot interactions with no ongoing relationship or reputation stake
• Disputes requiring physical evidence examination
• Disputes requiring witness credibility assessment beyond document analysis
• Disputes where parties have grossly asymmetric resources and no balancing mechanism
• Disputes implicating criminal liability or government action
• Disputes where enforcement requires legal compulsion unavailable to this system
• Fully adversarial environments where parties have no stake in system continuation

Known limitations:

• Jurisdictional constraints: Enforcement depends on mechanism integration; cross-border enforcement and enforcement requiring legal authority depend on external systems
• Party asymmetry: Repeat players accumulate knowledge; well-resourced parties can submit more evidence and absorb costs; design mitigates but does not eliminate asymmetry
• AI capability limits: Current AI adjudicators have bounded ability to assess credibility, interpret ambiguous language, and handle novel situations; escalation to humans is a design feature, not a failure
• Confidence calibration: AI confidence scores are approximations; calibration methods have known limitations; confidence should inform, not dictate, escalation decisions
• Anonymization limits: Precedent anonymization reduces but does not eliminate re-identification risk; parties with dispute-specific knowledge may recognize cases
• Adversarial limits: Sophisticated, well-resourced adversaries may find exploits; system assumes most parties most of the time act within bounds

Connection to core invariants

This system instantiates the structural requirements of a high-trust regime:

1. Scoped assumptions: AI adjudicator authority is bounded by stakes, dispute type, confidence threshold, and party consent. Assumptions about party behavior (evidence authenticity, good faith) are backed by deterrence mechanisms, not relied upon unconditionally.
2. Compositional validity: Individual dispute resolutions compose into consistent precedent. Multi-party disputes decompose into bilateral findings that aggregate coherently. Adjudicator configurations compose (panel decisions, sequential review) without contradiction.
3. Cost asymmetry: Good-faith participation is inexpensive—submit evidence, accept outcomes, comply with decisions. Exploitation is expensive—fraud penalties, frivolity sanctions, reputation damage, exclusion. The asymmetry is structural, not dependent on detection of every violation.
4. Failure localization: An incorrect decision in one dispute is correctable on appeal without invalidating unrelated decisions. AI adjudicator error in one case does not discredit all AI adjudication. Precedent later found erroneous can be corrected without reopening all cases that relied on it.
5. Reversion capability: If AI adjudication loses legitimacy or exhibits systematic failure, the system can contract—increase human involvement, reduce stakes limits, narrow dispute types—without abandoning the rule sets, precedent corpus, or enforcement mechanisms. The regime degrades gracefully rather than collapsing.

The dispute resolution protocol achieves high trust not by assuming participants are trustworthy, but by constructing an environment where trustworthy behavior is rewarded, untrustworthy behavior is penalized, errors are correctable, and the system can adapt when assumptions prove wrong.