---
id: ch12-ai-ethics-dashboard
type: spine
title: "The AI ethics dashboard: each pilot rates itself, principle by principle"
readingTime: 3
standalone: true
core: false
teaser: "Eight questions, three ratings, one form per pilot. The template that turns AI principles into something a region can check."
parent: null
diagram: null
recallQ: "What does the PoliRural+ AI ethics dashboard ask a pilot to do?"
recallA: "For each of the eight principles the pilot answers a guiding question about its current practice (for example how it handles bias, limitations or hallucination) and rates itself as fully integrated, needs improvement or not implemented. One form per pilot; a companion form collects one local story of up to 250 words per principle."
highlights:
  - "Official title: PoliRuralPlus AI Ethics & Participatory AI Maturity Tracker (WP2)"
  - "Eight guiding questions, three ratings: fully integrated, needs improvement, not implemented"
  - "A story form asks for one local story per principle, up to 250 words"
  - "The filled rows in D2.3 are labelled fictionalised illustrations, not results"
status: accepted
concept: ai-ethics-dashboard
conceptTitle: "The AI ethics dashboard for pilots"
parents: participatory-ai-principles
lang: en
lens: generic
depth: standard
formalism: none
visuality: balanced
lengthBand: standard
genre: explainer
state: edited
carriers: prose|table
---

D2.3 proposes a self-assessment for every pilot region, the **PoliRuralPlus AI Ethics & Participatory AI Maturity Tracker**. Its stated objective is to standardise how each pilot reports its alignment with the [Participatory AI Principles](#c/participatory-ai-principles).

## What the form asks

One form per pilot. For each principle the pilot describes its current practice in answer to one guiding question, then picks a rating: **Fully Integrated, Needs Improvement or Not Implemented**.

| Principle | Guiding question (D2.3, Table 70) |
|---|---|
| Transparency & Explainability | How do you explain AI decisions to users? |
| Stakeholder Co-Creation | How were stakeholders engaged in tool or policy design? |
| Multi-Actor Involvement | Who is involved across the quadruple helix? |
| Iterative Feedback | How is stakeholder or user input fed back into tools or policies? |
| Ethical & Responsible Use | How do you address issues like bias, limitations or hallucination? |
| Capacity Building | What training or support did you offer to stakeholders? |
| Inclusive Policy Design | How was AI used to shape foresight or RAPs inclusively? |
| Gender Equality & Social Justice | Gender and other diversity indicators for social justice (a field description, not a question) |

The overview table then gives each pilot an overall status. The template uses **In Progress, Mature and Developing**.

A companion form, **"AI in Action for My Region"**, collects evidence: the pilot region, the principle featured, a title, a local story of **at most 250 words**, an optional stakeholder quote and an optional image. The aim is one story per principle per pilot.

## Illustrations versus results

D2.3 fills the template with sample rows: a Monaghan row marked "needs flagging bias issues", a Slovak row with "feedback not looped yet". D2.3 itself labels these **fictionalised summaries** that show how pilots might submit content. They are not assessments.

The real material is in D2.3 §2.8. Pilots submitted **six "AI in Action" stories** through an online form, from Päijät-Häme (Finland), Vidzeme (Latvia), Slovakia (two stories), the Czech-Bavarian border and Monaghan (Ireland). D2.3 reports that the stories cover seven of the principles and three services: the Advisor, MAAT and custom GPT-based indicator tools.

## Why a form helps

D2.3 also proposes linking the principles to project KPIs: capacity-building KPIs to principle 6, policy co-creation outcomes to principles 2 and 7, and stakeholder feedback mechanisms to principle 4. A rating means something only with the practice behind it, which is why every row asks "how".

To start in your own region, fill the rows where you have a story first, then take the weakest remaining row as your next task.

**Sources:**
- D2.3 Rural-urban data analysis, §2.3 AI Ethics Dashboard Template for Pilots (Table 63); §2.7 Potential pilot stories and dashboard entries (Tables 65–69, marked as fictionalised); Template 1 Local Story Collection; Template 2 Pilot Input Form for Ethics Dashboard (Table 70); §2.8 Actual Pilot Stories; §3.2 Integrate Participatory AI into KPI & Impact Reporting — [PDF](https://www.poliruralplus.eu/docs/382/D2.3_Rural_Urban_data_analysis_v1.0.pdf)