---
id: ch12-participatory-ai-principles
type: spine
title: "Eight Participatory AI Principles for rural-urban planning"
readingTime: 3
standalone: true
core: false
teaser: "PoliRural+ wrote down how its AI tools may be used in planning. The short version: AI amplifies local knowledge, and people decide."
parent: null
diagram: null
recallQ: "What are the PoliRural+ Participatory AI Principles, and which premise ties them together?"
recallA: "D2.3 lists eight principles: transparency and explainability, stakeholder co-creation, multi-actor involvement, iterative feedback, ethical and responsible use, capacity building, inclusive policy design, and gender equality and social justice. They rest on the premise that AI amplifies local intelligence and does not replace it, so people question the outputs and make the decisions."
highlights:
  - "D2.3 (August 2025) sets out eight principles, each tied to a concrete tool or practice"
  - "The premise: AI amplifies local intelligence, it does not replace it"
  - "AI responses must be understandable and questioned, not blindly accepted"
  - "Policy Brief No. 1: AI quality rests on months of human data curation that is rarely funded"
status: accepted
concept: participatory-ai-principles
conceptTitle: "Participatory AI Principles"
parents: polirural-plus-project
lang: en
lens: generic
depth: standard
formalism: none
visuality: balanced
lengthBand: deep
genre: explainer
state: edited
carriers: prose|table
---

The July 2025 WP2 progress meeting decided that the project should position itself as a leader in **ethical, EU-aligned AI use in rural-urban planning**. The team then used the [PoliRural+ Advisor](#c/polirural-advisor) itself to analyse the project's outputs up to August 2025 together with the EU AI Act and EU guidelines on generative AI. The result, published in D2.3 as part of the **PoliRuralPlus AI Action Plan**, is a set of Participatory AI Principles.

## The eight principles

| # | Principle | What it asks (D2.3) | Where it shows |
|---|---|---|---|
| 1 | Transparency & Explainability | AI responses must be understandable and questioned, not blindly accepted | Advisor guidance prompts; JackDaw source referencing |
| 2 | Stakeholder Co-Creation | Services are tailored with regional input to local needs and goals | Advisor-based RAP drafting, stakeholder inputs |
| 3 | Multi-Actor Involvement | Civil society, business, academia and government all take part | MAAT, stakeholder panels |
| 4 | Iterative Feedback | Systems improve through continuous input and critique from users | VULTURE feedback loop |
| 5 | Ethical & Responsible Use | AI is used cautiously, recognising limits such as bias or hallucination | Bias and hallucination risks flagged in Advisor and JackDaw |
| 6 | Capacity Building | Digital literacy and training for rural actors | MAAT workshops, Advisor usage sessions |
| 7 | Inclusive Policy Design | AI supports inclusive co-creation of policies and foresight | RAP services, Advisor-enabled co-drafting |
| 8 | Gender Equality & Social Justice | Participation data disaggregated by gender and other diversity indicators | Workshop and sub-project reporting |

The press texts in D2.3 still speak of "7 principles"; the eighth appears in the principle table, the dashboard and the story forms. Count eight.

## One premise behind them

D2.3 states it plainly: the project is built on the premise that **AI amplifies local intelligence, not replaces it**. The principles turn that premise into checks. A planner must be able to see why the Advisor suggested something (1) and push back (4). The people who will live with a plan shape the questions (2, 3, 7), and someone has to teach them how (6). Who took part is counted, not assumed (8). The [multi-actor approach](#c/multi-actor-approach) is the method behind 2 and 3.

## Why planning needs this

Policy Brief No. 1 (June 2025), built on findings from all nine pilots, shows why good intentions are not enough. AI systems **remain highly dependent on the quality and structure of the data they ingest**, and feeding rural, multilingual data into the project's AI took **months of manual annotation, formatting and validation**. Repeated engagement requests through dashboards and co-creation tools caused **consultation fatigue** in several areas. A tool that hides its sources or asks the same villagers for input again and again would deepen the [fragmentation](#c/rural-urban-fragmentation) the project set out to reduce.

The principles are the yardstick for the rest of this chapter. The next block holds them against the EU AI Act.

**Sources:**
- D2.3 Rural-urban data analysis, §2 PoliRuralPlus AI Action Plan (Aug 25); §2.1 PoliRuralPlus Participatory AI Principles and Framework (Table 62); §3.2 Plan to strategically position PoliRuralPlus using its Participatory AI Principles — [PDF](https://www.poliruralplus.eu/docs/382/D2.3_Rural_Urban_data_analysis_v1.0.pdf)
- D7.3 Policy Brief No. 1, Annex 10 AI-Enabled AKIS, FAIR Data and Carbon-Smart Food Systems for Resilient Rural–Urban Europe — [PDF](https://www.poliruralplus.eu/docs/349/D7.3_Policy_Brief_No.1.pdf)