Cover image: AI-generated illustration of dialogue about technology with fictional people; not a photograph of the event.
Anyone receiving public funding to research or develop new technology should explain to the public, in understandable language, what they are working on. For me, one page would be the minimum. What problem is the technology meant to solve? What can it demonstrably do, where are its limits, and how can the people affected have a say?
I have been making this proposal for years. At the UN Digital Cooperation Day in New York, I raised it again. For me, it captures a practical task of Robotic & AI Governance: people need to be able to assess technology and express what they want while important decisions are still open.
An understandable text is the starting point. Participation begins when questions and objections receive an accountable response: who made the decision, and what changed as a result of the feedback?
In this article: first, the documented outcomes from New York and the original recordings; then my personal commentary and a practical proposal for publicly funded technology.
Go to the outcomes · Go to my one-page proposal · Go to the questions and answers
Event: 21 September 2026 · Research and source review: 18 October 2026
On 21 September 2026, the UN Office for Digital and Emerging Technologies (ODET) held the second UN Digital Cooperation Day in New York. During the high-level week of the UN General Assembly, the event brought together three tasks: assessing scientific evidence, informing policy decisions, and building the capacity for wider participation in AI. The official programme documents the sessions and recordings.
| Topic | What is available | Status and significance |
|---|---|---|
| National AI strategies | Introduction of the UN Blueprint for National AI and Data Strategies, drawing on experience from Lesotho and Guinea | A practical guide to implementing national strategies. Lesotho had already piloted it in August. |
| Scientific risk assessment | The first thematic brief from the Independent International Scientific Panel on AI | Released on 21 September as an advance unedited version; its focus is AI agents, misalignment and risks of losing human control. |
| AI and disaster risk reduction | A new partnership between ODET and the UN Office for Disaster Risk Reduction (UNDRR) | An announced collaboration on using AI to reduce disaster risk. |
| Work, open AI and cross-border testing | Discussions of skills, open-source AI and shared sandboxes for AI governance | Topics for exchange; a session alone does not amount to an adopted international standard. |
Sources for the overview: UN event report, ODET report on Lesotho's pilot, and the AI Panel's first thematic brief.
The Blueprint starts with a practical question: how can a national AI strategy become something a country can actually implement? In Lesotho, the work covered computing capacity, energy, connectivity, data, skills and financing. The August workshop produced a draft national roadmap for computing capacity. The New York launch brought together Lesotho's experience and Guinea as the next country to apply the approach. The ODET report describes that process.
The new thematic brief examines how the behaviour of capable AI agents can diverge from human intentions. It takes a documented incident during the safety evaluation of such systems as its starting point. The Panel places the incident in its research context and considers safeguards used in other fields with demanding safety requirements.
The limits of the assessment matter: the brief estimates neither the probability nor the timing of a severe future loss of control. It does not prescribe binding policy measures. The original publication is labelled an “advance unedited version”.
The preliminary overall report, released on 1 July 2026, and the new thematic brief serve different purposes. The overall report assesses the field more broadly; the September brief explores a particular risk question. The scientific Panel's FAQ explain this sequence of publications. My earlier assessment of the preliminary UN AI report remains available as a contribution written at that time.
The session on the future of work focused on education, skills and the readiness of people and institutions. This included the question of who can participate in an AI-enabled economy. Recording: work and education.
The “Panel of Panels” compared international scientific assessments: where do they agree, where are the gaps in knowledge, and which perspectives are missing? Experiences from the Global South formed part of that discussion. The focus was on improving the evidence available for governance. Recording: Panel of Panels.
The day brought together a practical guide, a new scientific publication and an announced partnership. Other sessions provided opportunities to exchange experience on implementation and cooperation. The official event report places these steps in the context of implementing the Global Digital Compact. It does not document a decision adopting my one-page proposal.
The UN programme links to three parallel recordings. The timestamps provide starting points for the main topics in the Oasis stream; the room links also give access to the other sessions.
Four starting points in the Oasis stream:
| Topic | Starting point in the recording |
|---|---|
| National AI strategies and the Blueprint | from 01:11:44 |
| AI, work, education and skills | from 02:47:32 |
| Introduction of the first scientific thematic brief | from 05:42:11 |
| Building a shared scientific evidence base: Panel of Panels | from 06:07:11 |
The timestamps refer to elapsed video time and mark the beginning of each topic.
My Delphi study is now also listed as an initiative in the UN Digital Cooperation Portal: Robotics & AI 2050+. The direct entry in the MUDT/TUM profile describes a global, independent study of the future of robotics and artificial intelligence. It brings together expert assessments and covers six enabling technological capabilities and nine application domains.
The Portal maps the initiative to areas including international AI governance and inclusion in the digital economy. The study remains an independent research undertaking; the listing makes it discoverable in the international cooperation directory.
For me, these two tasks complement each other. The study aims to develop a systematic understanding of experts' views of the future. The one-page proposal aims to enable people outside the specialist community to contribute their own questions and expectations.
The UN Digital Cooperation Day 2026 was an event organised by the UN Office for Digital and Emerging Technologies on 21 September in New York. It focused on science, policy and capacity-building for a global AI future. Governments, researchers, businesses and civil society came together to exchange perspectives. The official programme sets out the context and individual sessions.
Documented outcomes include the launch of the UN Blueprint for National AI Strategies, the scientific AI Panel's first thematic brief, and a new ODET-UNDRR partnership on AI and disaster risk reduction. Topics discussed also included work, open AI and cross-border sandboxes. The UN event report and the thematic brief's publication page document these different kinds of outcomes.
The official materials reviewed here document practical guidance, scientific assessment, cooperation and discussion. They do not record the adoption of a binding global AI law or of my one-page proposal. The precise document type and its status therefore matter. A scientific assessment informs policy decisions; my proposal for publicly funded technology remains clearly marked as personal commentary. Sources: UN event report, the Panel's mandate and publications.
The preliminary report of 1 July 2026 assesses AI's opportunities, risks and impacts more broadly. The first thematic brief, released on 21 September, focuses on AI agents, misalignment and risks of losing human control. It explores one question in greater depth rather than simply replacing the overall report. The Panel explains its publication sequence in its own FAQ. My earlier blog post about the overall report retains its historical context.
The UN Digital Cooperation Portal lists the global, independent Delphi study “Robotics & AI 2050+” as an active initiative in the MUDT/TUM profile. The entry describes its research approach and maps it to objectives of digital cooperation. This makes the initiative discoverable in the cooperation directory. The listing alone does not establish particular findings or the completion of a new survey round. The details are available in the direct UN Portal entry.
Official recordings are available for Oasis, Bamboo North and Bamboo South. In the Oasis stream, the timestamps above lead directly to national AI strategies, work and education, the scientific thematic brief, and the Panel of Panels. To hear individual statements in context, viewers can continue watching the full stream from those points.
I mean one understandable public page about a research or technology project. It explains the problem being investigated, why the approach was chosen, what demonstrably works, where the limits and risks lie, and how affected people can have a say. After reading it, people outside the field should be able to ask informed questions. The page complements scientific publications by providing an accessible way in for the public. I also explain the proposal in my own video post.
My proposal addresses anyone receiving public funding to research or develop technology. That explicitly includes universities and us as professors. I propose six answers: the problem and the people affected; the technology and its alternatives; demonstrated capabilities; limitations and what is not known; risks and responsibility; and opportunities to ask questions and influence decisions. This gives the public an understandable basis for judgement without requiring every reader to complete a specialist degree first.
Basic research can explain its scientific question, why it matters and what remains open. A later practical benefit may be unknown. That is precisely what the page should then say in understandable language. I do not want to force a supposedly certain everyday benefit that the research cannot yet demonstrate. An honest account of what is not known helps the public distinguish new knowledge, expectations and substantiated results.
My proposal calls for an understandable explanation within justified limits of confidentiality. Goals, expected benefits, demonstrated capabilities, open questions and responsibilities can often be described without disclosing protected technical details. Where information has to be withheld, the public explanation should make that boundary understandable. The aim remains to enable people to assess a project and ask questions about it; the page is not a complete technical disclosure.
I would initially integrate it into existing reports in one funding programme and update it at important milestones. What matters for the pilot is whether people outside the field can subsequently explain the project, recognise its limitations and direct a question to the right person. It should also assess whether feedback reaches the development process in time. For me, success means understandable information and workable routes to participation.
Participation begins when questions and objections can influence decisions that are still open. That requires accessible contacts, suitable opportunities for dialogue and accountable responses. People should be able to see what changed because of their feedback, or why a decision was retained. Both technically informed objections and questions from people without specialist knowledge need a place. My articles on AI After 60 and Robotic & AI Governance develop this thinking further.
Dominik Bösl
Olympiastrasse 6a
DE-86179 Augsburg
Email info@boesl.org
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Part 2 · Personal Commentary: One Page That Gives People a Say
The following reflections express my own position. I raised the proposal at the event and want to put it up for discussion.
One proposal, several opportunities to raise it
I attended the UN Digital Cooperation Day in New York as an expert.
The visit included many interesting conversations with people who help shape the AI debate. I raised my one-page proposal in several settings: in conversations and in a contribution framed as a question to a panel.
My concern is a practical connection between research and the public. If we develop technologies that may change many people's everyday lives, those people need an understandable basis for forming a judgement. They also need to be able to raise their questions.
My proposal starts with something we can take responsibility for as researchers and developers: explaining what we do, what we know and what remains uncertain.
A scientific paper serves a different purpose from a public explanation
Scientific publications primarily address fellow specialists. They should make methods, results and conclusions open to scrutiny. That requires technical precision and the terminology of the field.
People outside that field need an understandable way in as well. They help fund research through their taxes. They encounter its applications in everyday life. Some benefit; others have to deal with consequences they did not choose.
That is why the proposal explicitly includes us as professors. We should be able to explain the problem we are working on, which expectations we consider justified and where we still have questions ourselves.
Writing one understandable page requires choices. What do people need to know to make sense of the project? Which terms need explaining? Which limitation must I avoid hiding behind an impressive demonstration?
Six questions for publicly funded technology
For me, the page should answer six questions.
1. What problem are we trying to solve, and who would benefit?
Start with the need. Who is meant to benefit from the project? What would an improvement look like? The explanation should also address who might face disadvantages or additional demands.
2. Why do we need this technology? What alternatives are available?
A technical possibility does not, by itself, justify its use. There may be a simpler way to solve the problem. A combination of technical and organisational changes may be needed. The public should be able to understand why a particular approach was chosen.
3. What demonstrably works already, and under what conditions was it tested?
A promise needs a basis. What has actually been tested? Under which conditions? How much of that can already be transferred to the intended application? Understanding the difference between an experiment and reliable operation helps people assess expectations.
4. What can we not do yet, and what do we not yet know?
An honest “We do not know yet” is a useful answer. It makes uncertainty visible. This includes technical limitations and open questions about the consequences of a possible deployment.
5. What risks do we see? Who decides on safeguards or stopping the project?
Identifying risks needs to affect the design. Who is responsible? What safeguards are planned? Who can intervene if assumptions no longer hold or problems arise? These responsibilities should be understandable too.
6. How can affected people ask questions and influence further development?
A contact address can be a starting point. What matters is what happens next: when can people contribute? Who answers their questions? How will they find out whether an objection changed a decision?
I also summarised the proposal and the six questions in my LinkedIn video post of 30 September 2026.
What this might look like in practice
Imagine a publicly funded research project developing a service robot for public buildings. Its purpose is to help visitors find their way. This is a hypothetical example to illustrate the proposal.
The public explanation could first identify the navigation problem being studied and the people the support is intended to help. It would then need to explain why a robot is being tested for this purpose. Better signs, an accessible building map or a staffed information point would be obvious alternatives to compare.
To explain its capabilities, the project would need to describe what works so far and under which conditions. Does the system recognise differently worded questions? Can it handle several languages? What happens when it does not understand a question? Which tasks will a person continue to handle?
If the project needs sensors or personal data, the explanation should describe why they are needed and which questions remain open. It should also make clear how people can reach their destination without using the robot.
Finally, the project needs feedback from everyday use: from visitors, employees and people whose needs may have been overlooked in the first design. Their suggestions should still be able to influence the design.
That turns a general announcement into a project people can discuss in practical terms.
Basic research needs room for what we do not know
For basic research, the honest answer to the question of practical benefit may be: we do not know yet. Advancing knowledge is a legitimate aim. I do not want every scientific question to have to justify itself prematurely with a supposedly certain business case or an immediately measurable everyday benefit.
Even then, it is possible to explain the question being studied, why it is scientifically interesting and what uncertainties remain.
My proposal does not require publishing trade secrets or security-sensitive details either. A public explanation must respect those boundaries. Anyone promising a benefit should, however, be able to explain the basis for that promise and what is still an expectation.
The page opens the conversation
One page alone does not create participation. I therefore want it to come with an accessible contact person and an accountable way of handling feedback.
What questions were asked? What objections were raised? Who decided how to respond? What changed, what was deliberately retained, and why?
The point is not to accept every suggestion. Participation becomes credible when decisions are explained and people can see what their contribution achieved.
This approach connects my thinking on Robotic & AI Governance with the practical approach in AI After 60: explain clearly, make independent checking possible and help people form an informed opinion.
The same question arises in social media regulation for young people: how can protection, education and self-determination work together? Support must fit the people and their circumstances. Understandable information and meaningful opportunities to have a say remain fundamental tasks.
A pilot measured by how well people understand
The objection “more bureaucracy” is justified. My proposal is therefore to integrate the page into existing project reports and update it at important milestones. I do not want an additional form that ends up circulating only between the project and its funding body.
I would start by piloting the requirement in one funding programme. For me, that includes testing the explanation with people outside the field. Can they describe the purpose in their own words after reading it? Can they tell what already works and what is still uncertain? Can they find the right route for asking a question?
It would be equally important to assess whether feedback arrives in time to influence further development. Those are the questions by which I would judge the pilot.
International scientific cooperation remains important. My assessment of the UN's scientific AI Panel considers that wider level. An understandable explanation of a particular project gives people access to decisions that affect them directly.
I would like us to start. Anyone receiving public funding should be able to give the public an understandable account of their work. One page is a manageable first step. Its value will depend on the conversation and participation we build around it.