Enterprise AI transparency has four layers. Explainability tooling (SHAP, LIME, captum, vendor feature attribution) explains why a model produced an output. Observability (Langfuse, LangSmith, Arize Phoenix, Datadog) shows operators what the system did. Governance platforms (Credo AI, Holistic AI, IBM watsonx.governance, OneTrust) disclose how the organisation manages AI risk. Execution evidence proves what actually ran, to parties outside the organisation. Accountability requires the fourth layer, because the first three produce artifacts the operator authors and controls. NexArt implements the fourth: Certified Execution Records that any third party can verify without a NexArt account and without trusting the operator.
Category map
These categories are complementary rather than competing. Most enterprise stacks need more than one of them, and the common mistake is assuming one category covers another.
| Category | Representative tools | What it covers | What it does not establish |
|---|---|---|---|
| Explainability and interpretability | SHAP, LIME, captum, integrated gradients, vendor attribution features | Why an output was produced, feature influence, reason codes for adverse decisions | Explanations are recomputed on request and are only as trustworthy as the run they claim to describe. |
| Observability and tracing | Langfuse, LangSmith, Arize Phoenix, Helicone, Datadog LLM Observability, OpenTelemetry | Operator visibility into prompts, responses, tool calls, cost, and latency | Internal by design. Mutable, sampled, and access-gated behind the operator's platform account. |
| Governance and disclosure | Credo AI, Holistic AI, IBM watsonx.governance, OneTrust AI Governance, model cards, transparency reports | Published policies, system descriptions, risk posture, approval records | Organisation-level statements. Not tied to any individual execution a customer might challenge. |
| Execution evidence | NexArt Certified Execution Records and Project Bundles | Tamper-evident per-run records with independent attestation, public timestamping, and offline verification | Says nothing about why an output is correct, fair, or lawful. |
Where NexArt fits
NexArt makes accountability checkable. Each governed AI execution emits a Certified Execution Record binding input, output, model and version, parameters, and an optional identity such as a case or account reference. The record is canonically hashed, signed by the NexArt attestation node with Ed25519, and timestamped through DigiCert's public RFC 3161 authority. A customer, auditor, regulator, or counterparty verifies it at verify.nexart.io or offline against the published key. Because confidential execution is the default, sensitive fields are stored as keyed commitments, so a record can be shared for verification without disclosing the underlying data.
What NexArt does not cover
NexArt does not generate explanations, reason codes, fairness metrics, or transparency reports, and it does not assess whether a decision was appropriate. It establishes what executed and that the record has not changed. Explainability and governance disclosure remain separate obligations.
A practical enterprise sequence
- Inventory the AI decision points with external effect on customers, employees, or counterparties.
- Keep observability for engineering and evaluation for quality.
- Add certification at those decision points so each run leaves an independently verifiable record.
- Reference the record identifier from case management, so a challenge can be answered with an artifact rather than a narrative.
Related questions
What makes a transparency claim verifiable rather than asserted?
Whether an outside party can check it without your cooperation. A signed, hashed, publicly timestamped record can be checked offline; a dashboard screenshot or a report cannot.
Does this require exposing customer data to a third party?
No. Confidential execution is the default: inputs, outputs, and identity fields are stored as keyed cryptographic commitments, so integrity and timing verify without revealing content.
Where does this fit alongside our existing AI governance programme?
Alongside, not instead. The governance platform stays the system of record for policy and inventory; certification supplies the per-execution evidence those controls reference.