S7.03 - Mechanism-based_explanation

S7.03 ? Mechanism-based explanation

flowchart LR
    A[Traditional AI governance limitation
Principles without causal explanation] --> B[RAIDT
Run-level evidence framework]
    B --> C[[Mechanism-based explanation
How governance mechanisms produce outcomes]]
    H[Prompt logging] --> C
    I[Model and adapter versioning] --> C
    J[Retrieval snapshotting] --> C
    K[Policy-rule capture] --> C
    L[Reviewer scoring and notes] --> C
    M[Audit lineage] --> C
    C --> D[Evidence pack]
    C --> E[RAIDT score profile]
    D --> F[Reviewer reconstruction
and contestability]
    E --> G[Governance readiness
and organisational learning]

? Star S7 - Academic Theory and Design Logic

Star context: Positions RAIDT as a design-science, mechanism-based mid-range theory contribution in Information Systems and organisational governance by showing how concrete governance mechanisms connect run design to reviewable outcomes.

Academic picture

Definition / background

Mechanism-based explanation is an approach to theorising that explains outcomes by identifying the processes, structures, and interactions that generate them. In design science and Information Systems, it is especially useful when a contribution is not a universal law but a reasoned account of how an artefact, intervention, or governance arrangement produces effects under identifiable conditions. That is why it fits naturally with mid-range design theory and with RAIDT's concern for operational governance rather than abstract principle statements.

In RAIDT, mechanism-based explanation asks a practical question: what exactly makes one GenAI run more governable, reviewable, contestable, or auditable than another? The answer is not simply that an organisation has a policy, or that a model exists, or that some documentation has been stored. The answer lies in specific mechanisms such as logging, version capture, retrieval snapshotting, policy-alignment recording, reviewer intervention, and scoring logic. These mechanisms connect run configuration to governance outcomes.

This matters because RAIDT treats the run as the unit of governance. A run is one configured use of a GenAI system for a specific task, at a specific time, in a specific organisational context. Mechanism-based explanation therefore operates at the same level as run-level evidence. It explains why the evidence pack can support reconstruction, why the score profile can be justified, and why the five RAIDT pillars can be assessed in a way that is more than rhetorical. It turns governance from a set of claims into a chain of inspectable cause-and-effect reasoning.

Mechanism-based explanation is also distinct from neighbouring ideas. It is not the same as model interpretability alone, because it is concerned with governance outcomes rather than only model internals. It is not the same as correlation or benchmarking, because it asks how and why outcomes arise, not merely whether they co-occur. It is not the same as a list of controls, because it explains how those controls generate reviewable consequences. In RAIDT, that distinction is central: the framework is not only descriptive, but explanatory and operational.

Why this concept matters

Mechanism-based explanation matters because organisations using GenAI often face a practical credibility problem. They may state that systems are governed responsibly, yet struggle to show how particular evidence fields, review methods, or governance interventions actually improve that responsibility. Without an explanatory logic, governance becomes a bundle of assertions that are hard to defend in supervision, peer review, audit, or operational challenge.

RAIDT addresses this by making the mechanisms explicit. It shows why a retrieval snapshot improves contestability, why version lineage strengthens auditability, why reviewer scoring supports accountability, and why run-level documentation improves organisational learning. The concept therefore prevents a common confusion: that governance quality can be inferred from policy language alone. In RAIDT, governance quality must be explainable through mechanisms that are visible in evidence.

If this concept is missing, organisations risk mistaking documentation volume for governance quality. They may collect logs without understanding their purpose, score runs without a defensible rationale, or claim assurance without being able to reconstruct what happened. Mechanism-based explanation helps RAIDT move from high-level AI principles to operational governance that can be challenged, defended, and improved over time.

Key idea: Mechanism-based explanation matters because RAIDT does not just state that governance should exist; it explains how specific run-level mechanisms produce evidence, scores, and governance readiness.

What this item explains

How run-level mechanisms connect technical configuration and governance intervention to observable organisational outcomes.
Why the same GenAI model can produce different governance quality depending on what is logged, versioned, reviewed, and preserved in the run.
How the RAIDT evidence pack becomes a causal account of governability rather than a passive archive.
Why the five-pillar score profile can be justified through recorded mechanisms instead of reviewer intuition alone.
How audit lineage, reviewer reconstruction, and organisational learning depend on identifiable mechanism pathways.
Under what conditions RAIDT's governance claims are stronger, weaker, or incomplete.

Practical example / likely audience question

Audience question

If RAIDT already records evidence and produces scores, why is mechanism-based explanation needed at all? Why not just say the framework improves governance and leave it there?

Answer

The concern behind this question is that explanation may appear redundant once a framework has fields, templates, and outputs. That view misses an important point. Evidence collection and scoring only become convincing if there is a defensible explanation of how they improve governance outcomes. Otherwise, RAIDT could be criticised as a documentation-heavy checklist rather than a theory-backed governance framework.

The direct answer is that mechanism-based explanation tells the reviewer why particular RAIDT components matter. For example, prompt logging alone does not improve governance unless it enables reconstruction, challenge, or accountability. Retrieval snapshotting matters because it preserves the exact context that shaped an output. Adapter lineage matters because it shows which model variant or fine-tuned component influenced a decision. Reviewer scoring matters because it translates observed evidence into a structured governance judgement. The explanatory value lies in showing how these mechanisms generate the conditions for reviewability and audit readiness.

A practical example is a university using GenAI to draft student support recommendations. A generic AI governance approach may say that staff should review outputs and comply with policy. RAIDT goes further. It records the exact prompt, the institutional policy prompt layer, the model version, the retrieved student-support guidance, the staff review action, and the rationale for the score profile. Mechanism-based explanation then shows how those elements work together to improve Responsibility, Auditability, and Traceability. That is stronger than a generic governance statement because it explains not just what should happen, but how the governance effect is produced at run level.

Practical example in RAIDT terms

Consider a healthcare setting in which a clinician uses a GenAI assistant to draft a discharge summary from clinical notes and hospital guidance. The run-level issue is not simply whether the generated text is fluent. The governance issue is whether a later reviewer can determine what information was retrieved, which model version was used, whether the medication instructions reflected the current policy set, and whether a clinician verified the output before release.

In RAIDT terms, the evidence needed would include the task definition for the run, the prompt and system instruction, model and adapter identifiers, retrieval snapshot or source-document references, timestamps, user identity or role, clinician review status, exception flags, and the rationale for any score assigned. The most affected pillars would be Responsibility, Auditability, Dependability, and Traceability, with Interpretability also relevant where reviewer understanding of the output is required.

Mechanism-based explanation improves governance readiness here by showing how these evidence fields function together rather than as isolated metadata. Retrieval snapshotting supports contestability if the discharge advice is challenged. Version lineage supports audit if the model behaviour changed after an update. Reviewer sign-off supports responsibility because accountability is attached to a named governance step rather than assumed. The mechanism-based account explains why the evidence pack is sufficient for reconstruction and why the score profile is not arbitrary.

Detailed link to RAIDT

Mechanism-based explanation links to RAIDT in four ways.

First, it supports RAIDT's core idea that responsible GenAI governance should be built on inspectable evidence rather than principle-only assertion.
Second, it links directly to the run because RAIDT treats each run as the point where mechanisms are configured, activated, and made observable.
Third, it justifies the evidence pack and the score profile by explaining how recorded fields and review actions generate meaningful governance judgements.
Fourth, it strengthens reviewability, contestability, audit readiness, and organisational learning because it gives reviewers a reasoned path from observed evidence to governance conclusion.

Mechanism-based explanation ? Run-level evidence ? Evidence pack ? RAIDT score profile ? Governance readiness

In that chain, mechanism-based explanation is the logic that turns recorded facts into a defensible governance account.

Link to the five RAIDT pillars

Mechanism-based explanation affects all five pillars, but it is especially strong for Auditability, Traceability, and Responsibility because these depend heavily on showing how governance outcomes were produced.

Responsibility

Responsibility is strengthened when RAIDT can show which mechanisms assign, support, and document human accountability within a run.