S7.06 - Mechanisms

S7.06 ? Mechanisms

flowchart LR
    A[Traditional AI governance problem
Principles and controls are often abstract] --> B[RAIDT
Run-level evidence framework]
    J[Practical settings and tools
Healthcare, finance, public services, wrappers, dashboards, logging] --> C[[Mechanisms
Causal processes that turn a GenAI run into governance action]]
    B --> C
    B --> D[Run-level evidence]
    D --> C
    C --> E[Evidence pack]
    C --> F[Five-pillar score profile]
    C --> G[Reviewability and contestability]
    E --> H[Reviewer reconstruction]
    F --> I[Governance readiness]
    G --> I
    H --> K[Organisational learning and intervention]
    I --> K

? Star S7 - Academic Theory and Design Logic

Star context: Explains RAIDT as a design-science, mechanism-based mid-range theory contribution for Information Systems and organisational governance, showing how evidence, scoring, review, and intervention are causally connected at run level.

Academic picture

Definition / background

In design and explanatory theory, a mechanism is the process through which one state leads to another. It answers the question of how and why an intervention, configuration, or set of conditions produces an observable outcome. In RAIDT, mechanisms are the causal and operational processes that transform a single generative AI run from a moment of system use into a reviewable governance object.

This matters because RAIDT is not framed only as a list of desirable properties. It is framed as a mechanism-based contribution: evidence is captured at run level, organised into a structured evidence pack, interpreted through the five RAIDT pillars, and then used to support judgement, escalation, improvement, or assurance. Mechanisms therefore connect constructs such as run, evidence pack, pillar score, intervention, and governance readiness into a coherent explanatory chain.

Mechanisms are distinct from constructs and artefacts. A construct names a key concept, such as a run or a pillar score. An artefact provides a concrete tool, such as a template, rubric, wrapper, or dashboard. A mechanism explains the process by which those elements work together to produce a governance effect. Within RAIDT, mechanisms belong centrally because the framework claims not only that governance evidence should exist, but that there is a reproducible way for evidence capture and evaluation to improve reviewability, contestability, audit readiness, and organisational learning.

Mechanisms also differentiate RAIDT from generic AI governance language. Many frameworks identify principles such as transparency or accountability, but they stop short of specifying how these are operationalised in a specific run of work. RAIDT fills that gap by treating mechanisms as the practical logic that links run-level evidence, evidence packs, score profiles, and governance response.

Why this concept matters

Mechanisms matter because they explain how RAIDT works in practice rather than merely what RAIDT values in principle. Without an explicit mechanism, a governance framework can become a static checklist, a policy statement, or a taxonomy of concerns. With an explicit mechanism, the framework can show how a configured run generates evidence, how that evidence is interpreted, and how organisational action follows.

This avoids a common confusion in GenAI governance: the assumption that responsible use can be achieved simply by publishing policies or naming principles. Organisations need to know what actually happens between system use and governance judgement. Mechanisms provide that missing middle. They specify the practical route from run configuration and evidence capture to scoring, review, and intervention.

If mechanisms are missing, the risk is that pillar scores become ungrounded numbers, evidence packs become passive archives, and governance claims become difficult to defend under scrutiny. For organisations using GenAI in real work, the mechanism is what makes governance operational, repeatable, and contestable.

Key idea: Mechanisms matter because they convert RAIDT from a set of governance intentions into a demonstrable process that links run-level evidence to accountable organisational action.

What this item explains

How RAIDT moves from a single configured run to a reviewable governance record.
How evidence capture, evidence architecture, pillar scoring, and intervention are causally linked rather than treated as separate tasks.
How the five RAIDT pillars become operational through repeatable assessment and response processes.
How reviewers, managers, auditors, or researchers can reconstruct what happened in a run and why a governance judgement was made.
How continuous improvement emerges from repeated runs, accumulated evidence packs, and targeted governance interventions.
Why RAIDT is a mechanism-based mid-range theory contribution rather than only a descriptive framework.

Practical example / likely audience question

Audience question

How does RAIDT actually work as a governance mechanism rather than just as another AI governance checklist?

Answer

The concern behind this question is that many governance frameworks identify good principles but do not explain the operational path from use of a GenAI system to a defensible governance judgement. The direct answer is that RAIDT works through a mechanism chain: it captures run-level evidence, structures that evidence into an evidence pack, applies a five-pillar scoring logic, identifies weaknesses or gaps, and then supports review, intervention, or improvement.

A practical example is a team using a GenAI system to draft internal policy guidance. RAIDT does not stop at saying that the output should be responsible or transparent. It records the specific run context, the prompt and configuration, relevant inputs, review actions, and any exceptions or concerns. That material is then scored across Responsibility, Auditability, Interpretability, Dependability, and Traceability, producing a profile that can be examined and challenged.

RAIDT handles this better than a generic AI governance approach because its mechanism is tied to a single run as the unit of governance. That means claims about governance are attached to evidence from actual use rather than broad statements about the tool in general. In viva, supervision, or organisational discussion, this gives a much stronger answer to the question of how governance is enacted rather than merely asserted.

Practical example in RAIDT terms

Consider a hospital department using a generative AI assistant to draft discharge summaries from clinician notes. A specific run involves one clinician, one patient case, one prompt template, one model version, and one set of contextual constraints. The run-level issue is that the output appears fluent, but it is unclear whether all clinically relevant instructions were preserved and whether the summary can be reconstructed later for review.

In RAIDT terms, the mechanism begins with evidence capture: the prompt version, model identifier, source note references, time of use, reviewer identity, approval action, and any edits made before the summary is accepted. The evidence pack brings those elements together so that the run is not treated as an isolated text output but as a documented governance event.

The affected pillars are Responsibility, because clinical accountability for approval must be clear; Auditability, because the run must be reconstructable; Interpretability, because reviewers need to understand why the output was accepted or edited; Dependability, because omissions or unstable behaviour matter for clinical safety; and Traceability, because the organisation must be able to connect the final summary to the generating run and its evidence trail.

The mechanism improves governance readiness by turning a potentially opaque drafting event into a reviewable case. Instead of asking only whether the output looked acceptable, the organisation can ask whether the run was sufficiently evidenced, whether the score profile reveals governance weakness, and what intervention is needed before wider deployment.

Detailed link to RAIDT

Mechanisms links to RAIDT in four ways.

First, mechanisms connect directly to RAIDT?s core idea that governance should be grounded in evidence from actual system use rather than in abstract declarations. They explain how RAIDT converts a governance ambition into a repeatable process.

Second, mechanisms link to the run because the run is the unit through which the process operates. A mechanism is only meaningful in RAIDT if it can describe how a specific configured use of GenAI is captured, assessed, and made available for review.

Third, mechanisms link to the evidence pack and score profile because these are the main outputs through which the process becomes inspectable. The evidence pack stores the relevant material, while the score profile expresses where governance strength and weakness appear across the five pillars.

Fourth, mechanisms link to reviewability, contestability, audit readiness, and organisational learning because they define how evidence leads to judgement and how judgement leads to action. This is what allows RAIDT to support both immediate review of a single run and longer-term improvement across repeated runs.

Mechanisms ? Run-level evidence ? Evidence pack ? RAIDT score profile ? Governance readiness

The chain matters because it shows that governance readiness is not assumed at the start. It is produced through an evidential mechanism that can be examined, challenged, and refined.

Link to the five RAIDT pillars

Responsibility

Mechanisms support Responsibility by making clear who initiated, reviewed, approved, escalated, or rejected a run and on what basis. Responsibility is strengthened when governance action is tied to documented roles rather than implied after the fact.