README.md

metadata

license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-classification
pretty_name: OracleEconLab Oracle Incentives and Accountability v1
size_categories:
  - n<1K
tags:
  - blockchain
  - oracle
  - economics
  - trustworthy-ai
  - tabular
configs:
  - config_name: abc_dictionary
    data_files: data/abc_dictionary.parquet
  - config_name: uma_real_episode
    data_files: data/uma_real_episode.parquet
  - config_name: uma_real_episode_abc
    data_files: data/uma_real_episode_abc.parquet
  - config_name: uma_public_rpc_episode
    data_files: data/uma_public_rpc_episode.parquet
  - config_name: uma_public_rpc_abc
    data_files: data/uma_public_rpc_abc.parquet
  - config_name: uma_decisions
    data_files: data/uma_decisions.parquet
  - config_name: uma_decision_splits
    data_files: data/uma_decision_splits.parquet
  - config_name: decision_evidence
    data_files: data/decision_evidence.parquet
  - config_name: ai_feature_dictionary
    data_files: data/ai_feature_dictionary.parquet
  - config_name: public_ai_episodes
    data_files: data/public_ai_episodes.parquet
  - config_name: public_ai_decisions
    data_files: data/public_ai_decisions.parquet
  - config_name: public_ai_splits
    data_files: data/public_ai_splits.parquet
  - config_name: public_ai_evidence
    data_files: data/public_ai_evidence.parquet
  - config_name: public_ai_predictions
    data_files: data/public_ai_predictions.parquet
  - config_name: public_ai_metrics
    data_files: data/public_ai_metrics.parquet

OracleEconLab Oracle Incentives and Accountability v1

This review-sized draft turns fixed, public UMA protocol evidence into complete or right-censored economic lifecycles. It is a release candidate for the paper Who Verifies Decentralized Information? Economic Incentives, Accountability, and Trustworthy AI Across Oracle Protocols.

Dataset configurations

Configuration	Rows	Observation unit	Purpose
abc_dictionary	28	economic variable	Formulas, units, sources, missing rules and A/B/C family
uma_real_episode	1	cross-chain episode	End-to-end Polygon OOV2 to Ethereum DVM validation
uma_real_episode_abc	28	case-measure status	Honest mapping of every A/B/C measure to the real case
uma_public_rpc_episode	1	cross-chain episode	Clean-room episode reconstructed without curated-ledger input
uma_public_rpc_abc	28	case-measure status	Public-source-only A/B/C mapping and explicit unavailable states
uma_decisions	810	challenged proposal-time decision	Minimal four-action AI evaluation
uma_decision_splits	810	decision split assignment	Frozen chronological train/validation/test partition
decision_evidence	2430	timestamped evidence reference	Prediction-level provenance and leakage audit
ai_feature_dictionary	16	decision-time feature	Feature meaning, source and availability
public_ai_episodes	64	public-source cross-chain episode	Economic lifecycle reconstructed from fixed public receipts
public_ai_decisions	64	proposal-time decision	Credential-free public-source AI demonstration inputs
public_ai_splits	64	split assignment	Label-free chronological 44/10/10 partition
public_ai_evidence	192	timestamped evidence reference	Decision-time traceability for every demonstration prediction
public_ai_predictions	70	model/action output	Four-action predictions, confidence, evidence and protocol endpoint
public_ai_metrics	7	model evaluation	Calibration, coverage, abstention and stylized economic cost

A/B/C economic measure families

• A — Verification concentration (Who verifies?): HHI, top-k share, effective number of monitors and threshold monitor count.
• B — Incentives and frictions (Why verify?): reward/bond, capital-days, fees, Gas, verification cost and scenario-based expected private utility.
• C — Accountability outcomes (What happens?): dispute, adjudication, transfer, realized payoff, delay and right censoring.

A/B/C are measure families. They are not the project's Sample A/B/C data cohorts. Identity, decision-time, cross-chain-link and provenance fields remain supporting fields rather than being mislabeled as economic outcomes.

Construction and provenance

The fixed cutoff is 2026-06-30T23:59:59Z. The real case begins with canonical Polygon request, proposal, dispute and settlement records, links them to the Ethereum DVM with a Grade-A deterministic match, reconciles token flows, and separates returned principal from realized reward. Every processed episode or decision contains transaction/evidence references and a deterministic provenance identifier. File integrity is recorded in checksums.csv.

The main reconstruction commands live in the GitHub real-release-v1.5 branch. public_evidence/ includes the five fixed receipts, canonical headers, case specification, source checksums and verified contract-semantic rules used by the clean-room entry. This draft contains review-sized processed evidence; it does not claim that every protocol ledger can be regenerated without archive infrastructure.

The executed notebooks/uma_minimal_trustworthy_ai_demo.ipynb loads the frozen tables, reruns the leakage audit and chronological evaluation, and writes the review-sized artifacts under demo_outputs/. Its reported performance is not edited or filtered after execution.

The independent public_ai_evidence/ path closes the public-source chain for a fixed 64-episode demonstration: a label-free transaction registry, 280 canonical Polygon/Ethereum receipt and header bundles, decoded economic episodes, decision-time features, model outputs and QC. Live mode uses credential-free public endpoints; offline mode deterministically replays the frozen snapshot. The model result is intentionally reported even when weak. It is a pipeline demonstration rather than evidence of production challenge quality.

Trustworthy-AI task

At the OOV2 proposal timestamp, the benchmark emits one of Accept, Investigate, Challenge, or Abstain. The endpoint is whether the later UMA protocol resolution rejects the proposal. Only decision-time fields may be used as model inputs. Columns whose names end in _outcome_only, the protocol label, dispute result and settlement evidence are evaluation-only and must not enter the model.

The 810 observations condition on proposals that were actually challenged, Grade-A linked, price-consistent and flow-exact. The task is therefore a conditional protocol-outcome evaluation, not a causal policy for all proposals and not an independent truth classifier.

Intended uses

• audit economic variable construction and source traceability;
• reproduce the real UMA episode and its payoff decomposition;
• evaluate calibration, abstention, leakage and robustness on the conditional decision cohort;
• study A/B/C constructs within their declared observation units.

Uses that are not validated

• production deployment of automated challenges;
• causal claims that rewards or penalties improve truthfulness;
• treating protocol acceptance or DVM resolution as independent factual truth;
• identity, geography or intent inference from blockchain addresses;
• aggregation of heterogeneous token amounts without asset-preserving conversion.

Biases and limitations

The decision cohort has selective labels because it includes only actually challenged UMA requests. It over-represents mechanisms and market adapters for which exact cross-chain and token-flow evidence is recoverable. Address-level concentration is not entity-level concentration. Off-chain investigation labor is unavailable and remains a declared scenario. Independent factual truth is available only for a small external-rule subset outside the core protocol label.

Personal and sensitive information

The data contain public pseudonymous contract/account addresses, transaction hashes and block metadata. They contain no asserted mapping from an address to a natural person and must not be used to infer human identity. RPC endpoint URLs, credentials and private keys are not included.

Licensing

Original curated tables and documentation are CC BY 4.0. Upstream contracts, APIs and third-party records remain governed by their own terms. See DATA_LICENSE.md and CITATION.cff.