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PALOPALO FRAMEWORK

Community & Beyond PALO

Better AI governance
starts with people.

People who question assumptions. Connect disciplines. And turn responsible AI into work we can examine, improve and share.

Find your contribution Meet the team

An open invitation to universities, independent researchers, auditors and builders.

Different disciplines. A shared purpose. Research, engineering and independent review come together around open AI governance. PALO OPEN GOVERNANCE Research Engineering Review
Different disciplines. A shared purpose.
Open source. Open questions. Shared progress.PALO v3.1.0

01 / The people behind the work

Different expertise.
One shared commitment.

Connecting governance, systems engineering and standards to make AI decisions more accountable.

Creator & maintainer

Fabrizio Degni

Fabrizio Degni conceived and created PALO Framework and continues to maintain the project, including its methodology, software and documentation. PALO was also the subject of his doctoral thesis in Computer Science at the European Institute of Management and Technology (EIMT). PALO welcomes community contributions and review.

Systems architecture
Research & AI standards

Alessandro
Simonetta

An engineer and university lecturer since 1999, Alessandro teaches Computer Systems Architecture at the University of Rome Tor Vergata. Following 24 years at INAIL, he served as IT Director at the Italian Space Agency. His standards work spans software engineering, information security and AI.

In PALO

Systems architecture, technical research and AI standardisation: connecting the governance approach to engineering questions and reviewable technical proposals.

Explore the agentic technical profile

Standards & data quality
Bias evaluation

Maria Cristina
Paoletti

Maria Cristina is an INAIL Coordinator in the Pricing and Benefits Sector and a UNINFO contributor to software and systems engineering standards. Since 2022, she has served as a liaison with CEN-CENELEC JTC 21 and the UNI/CT 533 AI mirror committee.

In PALO

Standards, data quality and bias evaluation: bringing a measurement perspective to governance evidence and the discussion of trustworthy AI.

Explore the data assurance approach

Biographies adapted from the team’s AI Office presentation. Professional affiliations describe individual experience; participation in PALO does not imply institutional endorsement.

02 / An open invitation

There is room
for your perspective.

You do not need to contribute code to move PALO forward. Bring a research question, a critical review, a practical use case or the resources to help build.

Read the contribution guide

A useful first contribution can be small: one assumption challenged, one test reproduced, one explanation made clearer.

01Universities & research labsTurn a question into a reproducible study.

Explore thesis topics, comparative evaluations and research collaborations. Start with one governance question, a method and an artifact others can inspect.

Propose a research collaboration ↗Explore the evaluation protocol
02Independent researchersChallenge assumptions. Make findings reusable.

Reproduce a result, examine a source or propose a new method. Independent work is welcome: describe the question, evidence and limitations in a contribution proposal.

Start a contribution proposal ↗
03Auditors & independent reviewersHelp make evidence worth trusting.

Review control mappings, evidence traceability and assurance claims. Propose a defined review scope and disclose relevant conflicts of interest. This is an invitation to contribute review, not a certification programme.

Propose a review ↗Explore Audit Bench
04Engineers, designers & buildersMake the framework easier to use and extend.

Improve integrations, tests, accessibility, documentation or user experience. Start with a focused issue and a small, reviewable change.

Find or open an issue ↗
05Practitioners & civil societyBring the context that a framework can miss.

Contribute sector experience, rights questions or a safely shareable case. Help identify where governance language is unclear and where real decisions need better support.

Share a question or use case ↗
06Support the developmentGive open work the capacity to grow.

Offer research time, engineering expertise, infrastructure or help exploring grants. Let’s define a useful contribution, shared expectations and an achievable outcome together.

Discuss development support ↗

03 / Beyond PALO

The work is bigger
than one framework.

Open governance becomes useful when ideas travel: across research, standards, tools and the communities they serve.

Interoperability / Open proposal

Connect approaches.
Make evidence travel.

Explore how PALO evidence objects and decision gates could connect with Microsoft Agent Governance Toolkit. A proposal for technical review, with integration still to be validated.

Read the proposal ↗Join the discussion ↗
Research / Public-interest dialogue

Bring technical questions
into a wider conversation.

The European agentic governance discussion proposal connects delegated action, accountability and a comparative evaluation plan. It is a starting point for critique and collaboration.

Explore the discussion proposal ↗Open the team presentation ↗
Shared knowledge / Open practice

Build on the work.
Share what you learn.

Use the source, methods and documentation to ask better questions. Contribute improvements that other researchers and practitioners can inspect and reuse.

Explore the open source ↗Browse the documentation ↗

These are collaboration routes and public proposals. They do not represent institutional partnerships or endorsements.

04 / Let’s start a conversation

Bring a question.
Build something useful.

Tell us what you care about, what you bring and where you would like to contribute. We can shape the next step together.

Start a conversation

paloframework@gmail.com

  1. 01

    Choose a question

    Find a contribution route that fits your interests.

  2. 02

    Define a first step

    Agree a useful scope, artifact and way to collaborate.

  3. 03

    Share and improve

    Make the work reviewable and learn from feedback.

Prefer to start in the open?

Contribute on GitHub