Responsible AI Governance Dashboard
A dashboard concept that helps organizations monitor AI systems for risk, compliance, human oversight, data quality, and user trust.
Try the live demo- Role
- AI Governance Product Designer
- Project type
- Governance & Product Concept
- Duration
- 4 weeks
- Tools
- Figma, Miro, Notion, Excel
01
Executive Summary
The dashboard gives an organization one place to answer the questions an auditor, a data protection officer, or a board member will ask: which AI systems do we run, who owns them, what risk level do they carry, is oversight defined, and what has gone wrong so far.
02
Business Problem
Companies adopting AI often lack a central place to monitor AI risks, approvals, user feedback, compliance status, and model performance.
03
Stakeholders
- Data protection officer
- Compliance and legal
- System owners
- IT security
- Management and board
- Employees using AI systems
04
Discovery and Research
- Existing AI and automation tools in use, including unofficial ones
- Current approval process before launch
- EU AI Act classification needs
- Logging and retention practices
- How incidents are reported today
05
Objectives
- Maintain a complete inventory of AI systems
- Assign a risk level and an owner to every system
- Make the pre-launch checklist visible and auditable
- Track user trust signals over time
- Record incidents with root cause and action taken
06
Proposed Solution
A governance workspace with five sections: system inventory, risk classification, compliance checklist, user trust metrics, and incident log. Each system has an owner, a risk level, and a status, and nothing reaches production status without a completed checklist.
V
Visual analysis (D3)
Every figure below is generated with D3 from this case study’s own data and is annotated using Tamara Munzner’s what / why / how framework: the data abstraction, the abstract task it supports, and the visual idiom with its marks and channels.
V1Risk exposure by impact
- High
- Medium
- Low
What — dataItems are identified project risks; one ordered attribute (impact) and one categorical attribute (mitigation, on hover).
Why — taskRank the risks and identify the extremes that need mitigation first.
How — idiomDot plot: point marks, position on a common ordered scale as the primary channel, colour hue as a redundant encoding.
V3Implementation sequence
What — dataItems are roadmap phases with an ordered key (sequence) and a quantitative attribute (workstreams per phase).
Why — taskSummarise the delivery order and see where the workload concentrates.
How — idiomGantt-style ranged bars: line marks with length encoding magnitude, aligned on a shared horizontal scale, colour separating phases.
V4Solution architecture as a network
What — dataA node-link network: nodes are architecture components, links are data flow, and layer is a categorical attribute.
Why — taskExplore topology — trace a path from data source to governed output and locate dependencies.
How — idiomLayered node-link layout: rectangle marks positioned by layer, connection marks for flow, greyscale luminance separating adjacent layers.
V6Engagement depth profile — radar
- Documented items
What — dataOne item (this project) with seven quantitative attributes: the number of documented artefacts per workstream.
Why — taskSummarise the shape of the engagement and compare workstreams — which parts are deep and which are thin.
How — idiomRadar/star plot: line and point marks, angle channel for the attribute key, radial distance for magnitude.
V7Roadmap workload — circular barplot
- Workstream items
What — dataItems are roadmap phases with one ordered key (sequence) and one quantitative attribute (workstream items).
Why — taskCompare how much work each phase carries and see the cycle of delivery as a whole.
How — idiomCircular barplot: arc marks, angle for the ordered phase key, radial length for magnitude.
V8Architecture composition — treemap
- Component
- Layer
What — dataA hierarchy: architecture layers containing components, each component counting as one unit.
Why — taskSee where the system's weight sits — which layers hold the most moving parts.
How — idiomTreemap: containment for the hierarchy, area marks sized by component count, nested rectangles for layers.
07
System Architecture
+---------------------------+
| AI system inventory |
| name, dept, vendor, |
| data, owner, status |
+---------------------------+
|
v
+---------------------------+
| Risk classification |
| low / medium / high / |
| not allowed |
+---------------------------+
|
v
+---------------------------+ +----------------------+
| Compliance checklist | --> | Trust metrics |
| GDPR, DPO, oversight, | | acceptance, appeals |
| logging, AI Act, bias | | survey score |
+---------------------------+ +----------------------+
|
v
+---------------------------+
| Incident log |
| severity, cause, action |
+---------------------------+AI System Inventory
- System name
- Department
- Vendor
- Risk level
- Data used
- Owner
- Status
Risk Classification
- Low risk
- Medium risk
- High risk
- Prohibited / not allowed
Compliance Checklist
- GDPR review completed
- Data protection officer approved
- Human oversight defined
- User information available
- Logging enabled
- Vendor agreement reviewed
- EU AI Act classification completed
- Bias testing completed
User Trust Metrics
- Acceptance rate
- Rejection rate
- User complaints
- Trust survey score
- Explanation requests
Incident Log
- Incident date
- Description
- Severity
- Affected users
- Root cause
- Action taken
- Status
08
UX Design
The dashboard is designed for a non-technical owner. Status is expressed as a decision — approved, blocked, needs review — rather than as raw model metrics.
Screens
- Portfolio overview with risk distribution
- System detail with owner, data, and oversight description
- Checklist view with evidence attachments
- Trust metrics over time
- Incident log and follow-up actions
Key UX elements
- Risk badges
- Blocked-until-complete checklist
- Evidence links
- Quarterly review reminders
- Export for audit
10
Governance framework principles
| Principle | What it means in practice |
|---|---|
| Human oversight | A named person can stop or override the system |
| Transparency | Users are told when AI is involved and what it does |
| Data protection | Lawful basis, minimization, and retention are documented |
| Fairness | Bias testing on the groups the system affects |
| Accountability | One owner per system, recorded in the inventory |
| Security | Access control, secrets handling, and logging |
| Sustainability | Model and infrastructure cost and energy considered |
| Auditability | Decisions and overrides are reconstructable |
R1
Risk Analysis
| Risk | Impact | Mitigation |
|---|---|---|
| Shadow AI outside the inventory | High | Discovery sweeps, easy registration, procurement gate |
| Checklist becomes theatre | Medium | Evidence required per item, sampling audits |
| Incorrect AI Act classification | High | Documented reasoning, legal review, periodic reassessment |
| Stale data in the dashboard | Medium | Quarterly owner review with reminders |
R2
Implementation Roadmap
Weeks 1–2
Phase 1: Framework
- Risk levels
- Checklist definition
- Roles and accountability map
Weeks 3–4
Phase 2: Prototype
- Inventory and detail views
- Checklist flow
- Incident log
Months 2–3
Phase 3: Pilot
- Register existing systems
- Run first quarterly review
- Report to management
R3
Change Management
- Owner onboarding sessions
- Lightweight registration so compliance is easier than avoidance
- Quarterly governance review in the management agenda
- Clear escalation path for employees
R4
Success Metrics
Coverage
- Share of AI systems registered
- Share with a named owner
- Share with completed checklist
Compliance
- AI Act classifications completed
- GDPR reviews completed
- Open incidents older than 30 days
Trust
- Trust survey score
- User complaints
- Explanation requests
R5
Deliverables
- Dashboard concept
- Risk framework
- UI prototype
- Governance checklist
- Case study
“Responsible AI requires governance, transparency, and accountability.”
Next project
AI Readiness Audit & Transformation Roadmap