SDG Readiness Platform

*Making a Global Dialogue on AI Governance succeed* 1. *Define success upfront*: Don't aim for "one treaty to rule them all." Success = shared red lines, interoperable standards, and a standing forum for disputes. Publish concrete KPIs before day one: number of countries adopting baseline safety tests, time to coordinate on incidents, and funding committed for global AI safety research. 2. *Get the right people in the room*: Beyond US, EU, China. Include India, Brazil, Kenya, UAE, and the African Union as co-leads, not observers. Add the labs actually building frontier models, open-source communities, and civil society groups focused on labor and human rights. No legitimacy without representation from where AI will be used most. 3. *Start with narrow, high-consensus topics*: Focus first on what everyone fears: loss of control, bio-weapons, cyber attacks, and AI-enabled election interference. Build trust there before tackling economic competition or IP. Use technical working groups to agree on evals for CBRN risks and deepfake watermarking. Early wins unlock harder talks. 4. *Make it technical, not just political*: Anchor discussion in shared benchmarks, incident databases, and third-party audits. Create a "Geneva for AI" with joint red-teaming exercises. When diplomats see the same test results, ideology matters less. 5. *Fund capacity, not just rules*: Many states can't enforce what they sign. Commit a Global AI Governance Fund to help low/middle-income countries build regulators, run evaluations, and participate in standards bodies. Otherwise you get a two-speed world and defectors. 6. *Build in transparency and iteration*: Livestream plenaries, publish working drafts, and run a 12-month review cycle. AI moves faster than treaty law. The dialogue must be a process, not a one-off summit. 7. *Respect different values without stalling*: Allow a "plurality architecture." Agree on common harms to prevent, but let regions differ on speech, copyright, and competition. Interoperability beats uniformity.

• AI capacity-building
• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Open-source software, open data and open AI models

All 4 reasons stated above should be on the first priority list into AI Governance . Let's rethink on this most important issue of present times .

I think it is mostly covered

The *AI Dialogue* can act as the standing "switchboard" that keeps international AI governance moving between big summits and treaty talks. *1. Build a common fact base* Disagreements often start with different data. The Dialogue can host shared evaluations, incident reporting, and red-teaming results so all countries debate the same evidence. When labs, auditors, and states look at identical benchmarks for bio-risk or cyber misuse, political gaps shrink. *2. Coordinate fast on cross-border risks* AI incidents won't respect borders. The Dialogue gives states a pre-agreed hotline and response playbook. Think of it like IAEA for nuclear or WHO for pandemics, but lighter: a place to flag model releases, share post-mortems, and coordinate mitigations within days, not years. *3. Bridge regimes without forcing uniformity* The EU, US, China, India, and others won't adopt identical rules. The Dialogue's role is interoperability. It can map where rules align, draft mutual recognition for safety tests, and set minimum "no-go" lines everyone accepts, like no autonomous bioweapon design. That lets diversity coexist with baseline safety. *4. Include the builders and the affected* Treaties between states alone fail if the frontier labs and open-source communities aren't at the table. The Dialogue can convene technical working groups with engineers, civil society, and Global South regulators together. Policy written with builders is policy that actually gets implemented. *5. De-risk defection through capacity* Many countries can't evaluate models or enforce rules yet. The Dialogue should channel funding and expertise so more states can participate as rule-makers, not rule-takers. Broader capacity means fewer incentives to undercut shared standards.

*Initiatives to build on* - *GPAI (Global Partnership on AI)*: 29+ countries + experts doing applied projects on responsible AI, data governance, and future of work. - *OECD AI Principles & AI Policy Observatory*: Widely adopted principles + a live database of national policies and metrics. - *UN efforts*: Secretary-General's High-Level Advisory Body on AI, UNESCO Recommendation on Ethics of AI, and ITU's AI for Good. - *Standards bodies*: ISO/IEC JTC 1/SC 42, IEEE, NIST AI RMF — where technical benchmarks and risk frameworks are actually written. - *UK/AISI & US AISI Network*: Government-run frontier model safety testing, now expanding internationally. - *Hiroshima AI Process / G7*: Focus on advanced AI systems and a voluntary code of conduct. - *Frontier Model Forum + Partnership on AI*: Industry-led safety research, evals, and red-teaming norms. *What the AI Dialogue adds* 1. *Continuity between summits*: Most current work is annual or siloed. The Dialogue can be a permanent, lightweight secretariat that keeps working groups active month-to-month and prevents reset after every election cycle. 2. *Operational layer for incidents*: No existing body runs a 24/7 cross-border AI incident hotline. The Dialogue can host shared reporting, joint post-mortems, and rapid coordination playbooks — like CERTs do for cybersecurity. 3. *Bridge technical + political*: GPAI is expert-heavy, G7 is political, standards bodies are technical. The Dialogue can force them into the same room quarterly: regulators, lab CEOs, and Global South states testing the same evals and debating trade-offs with shared data. 4. *Legitimacy through inclusion*: Many initiatives are OECD- or US/EU-centric. The Dialogue should co-chair agendas with India, Brazil, AU, ASEAN from day one and fund their participation in standards and safety evals. That reduces fragmentation risk. 5. *Plurality, not uniformity*: Instead of pushing one rulebook, the Dialogue adds value by mapping interoperable baselines — "these harms we all prevent, these areas we regulate differently" — and brokering mutual recognition of safety tests.

*How stakeholders contribute* - *Governments*: Set red lines, fund third-party evals, and commit to incident reporting. Bring regulators, not just diplomats, so rules match technical reality. - *Frontier labs & open-source groups*: Submit models for independent testing pre-release, share post-mortem data, and adopt common safety benchmarks. Open-source communities contribute evals and threat models others miss. - *Standards bodies (ISO, IEEE, NIST)*: Provide the test harnesses. Turn Dialogue agreements into measurable specs labs can implement. - *Civil society & academia*: Audit impacts on labor, rights, and Global South contexts. Surface blind spots and run independent red-teaming. - *International orgs (UN, OECD, GPAI)*: Host policy memory and capacity building. Ensure small states aren't just observers. - *Industry users*: Banks, hospitals, manufacturers bring real deployment risks and demand signals for safety features. *Format & structure recommendations* 1. *Permanent, not episodic*: A small standing secretariat + quarterly plenaries. Avoid one-off summits that reset every year. 2. *Three-tier structure*: - *Principals Council*: 15-20 states + regional blocs, co-chaired by Global North/South, sets agenda. - *Technical Working Groups*: By risk domain — CBRN, cyber, elections, labor. Mixed: labs, auditors, regulators, civil society. Output is tests and thresholds, not communiqués. - *Incident Coordination Cell*: 24/7 contact points, shared taxonomy, and 72-hour response playbooks. 3. *Plurality by design*: Adopt "baseline + modules." All members enforce agreed minimums on catastrophic risk. Beyond that, mutual recognition lets regions diverge on speech, IP, competition. 4. *Evidence first*: No plenary debate without shared data. Each meeting starts with results from joint evals and incident reviews. 5. *Fund participation*: Travel + technical support for low/middle-income states so it isn't a G7 club. Tie funding to adopting shared reporting. 6. *Transparency default*: Livestream plenaries, publish working drafts, annual public audit of commitments kept. Goal: Make the Dialogue the place states and labs call before a crisis, not after.

*Who's missing from AI governance today* 1. *Global South regulators & practitioners*: Most frameworks are written in Brussels, D.C., and London. Countries in Africa, Southeast Asia, and Latin America will deploy AI at scale but often join talks as observers, not rule-setters. They lack funding to run evals or send technical staff to standards bodies. 2. *Workers & labor*: AI governance focuses on catastrophic risk and chatbots. Missing are the nurses, teachers, truck drivers, and gig workers whose jobs and wages shift first. Unions and informal worker networks are rarely at the table. 3. *Open-source & small builders*: Frontier labs dominate safety debates, yet open models and local startups will power most real-world use in low-resource settings. Their constraints and threat models differ from Big Tech. 4. *Marginalized language & cultural groups*: Eval datasets and safety testing are English-heavy. Low-resource language communities face different disinfo, bias, and abuse patterns that never show up in benchmarks. 5. *Disabled & elder communities*: Accessibility, assistive use, and elder-care automation are governance afterthoughts, despite high impact. 6. *Subnational governments & cities*: Mumbai, Lagos, and São Paulo will regulate AI use in policing, transit, and services before national law catches up. City-level input is absent. *How to include them* - *Fund seats, not just invites*: Pay for travel, technical staff, and eval infrastructure so participation isn't symbolic. Tie GPAI/UN funding to regional co-chair roles. - *Regional mini-Dialogues*: Run AI Dialogue chapters in Nairobi, Delhi, Jakarta, and São Paulo. Feed results into the global plenary instead of expecting everyone to fly to Geneva. - *Mandate representation in working groups*: Each technical group reserves slots for labor, open-source maintainers, disability advocates, and low-resource language experts. - *Build multilingual evals first*: Procure datasets and red-teaming in Swahili, Hindi, Arabic, Bahasa, etc. If you can't test it, you can't govern it. - *Create a "User Impact Council"*: Formal channel for worker orgs, city CIOs, and patient groups to file incident reports and trigger agenda items. Inclusion isn't charity. Without these voices, governance fails where AI is actually used.

*Formats that move beyond panels and communiqués* 1. *Live red-team wargames*: Put a frontier model, 3 national regulators, and civil society hackers in one room for 4 hours. Objective: find a misuse path. Stream the process, not just results. Nothing aligns incentives like watching your safeguards break in real time. 2. *Scenario studios*: Each Dialogue session starts with a 90-min "future cast." Example: "It's Nov 2027. A leaked audio of a PM rigged an election in your country." Mixed tables of states, labs, and media draft a response playbook. You debate trade-offs before the crisis, not after. 3. *Model & policy sandboxes*: Labs bring a pre-release model to a closed room. Regulators from 5 regions run their draft rules on it simultaneously. Output: which rules actually constrain risk, which just add paperwork. Builds shared tests faster than whitepapers. 4. *Reverse briefings*: Ban keynotes. Instead, Global South city officials brief frontier lab CEOs on how AI is breaking transit or welfare systems today. Then CEOs have 10 minutes to propose fixes. Flips the power dynamic and grounds talk in deployment. 5. *Incident replay rooms*: Take a real past incident — deepfake scam, biased welfare algorithm — and re-run it with Dialogue members playing different roles. Rotate roles next round. Builds muscle memory for coordination. 6. *Eval hackathons*: 48 hours, mixed teams of standards engineers, linguists, and labor reps. Task: build a benchmark for one under-tested harm in Hindi, Swahili, or Bahasa. Winning eval becomes part of the shared test suite. Turns talk into infrastructure. 7. *Deliberative polling with skin in the game*: Before voting on a norm, 100 participants get 2 hours with experts, then vote. But their vote is public and tied to their org. Raises the cost of posturing. 8. *Silent treaty drafting*: After debate, lock negotiators in a doc with track changes for 3 hours. No speeches allowed. Forces precision and reveals real red lines. *Why these work*: They replace statements with artifacts — tests, playbooks, and benchmarks. Engagement means leaving with something you can run, not just something you can quote.

*Concrete examples that work today* *Policies & laws* - *EU AI Act's risk tiers*: Bans unacceptable uses, mandates audits and transparency for high-risk systems, lighter rules for low-risk. Gives a clear ladder labs can plan against. - *NIST AI Risk Management Framework (US)*: Voluntary but widely used. Map-Measure-Manage-Govern structure lets companies integrate safety into existing compliance without new regulators. - *China's deep synthesis rules*: Requires watermarking and consent for AI-generated faces/voices. Early real-world test of provenance mandates. *Practices* - *Pre-deployment external red-teaming*: Anthropic, OpenAI, Google DeepMind now contract groups like METR and Apollo before major releases. Finds flaws internal teams miss. - *Model cards & system cards*: Standardized docs from Hugging Face and Google detailing data, limits, and evals. Readers know what they're getting. - *Incident databases*: http://OECD.AI and Partnership on AI run public logs of AI harms. Like aviation's ASRS, they let the field learn from failures without waiting for lawsuits. *Platforms & tools* - *MLCommons MLPerf Safety*: Emerging open benchmark suite for misuse risk, like cybersecurity's CTFs but for models. Gives apples-to-apples comparisons. - *Singapore's AI Verify*: Open-source testing toolkit governments and SMEs can run themselves. Lowers the cost of oversight. - *C2PA + watermarking*: Coalition for Content Provenance standards embed signed metadata in media. Adobe, Microsoft, and Truepic ship it now. Helps with disinfo. *Approaches* - *UK/US AI Safety Institute network*: Government teams that actually run evals, not just write rules. Share results across borders to avoid duplication. - *GPAI's project model*: Small, multistakeholder teams deliver tools like data-justice guidelines in 12 months. Fast, practical, not treaty-bound. - *Bug bounties for AI*: Anthropic and OpenAI pay researchers who find new jailbreaks or risks. Borrows from cybersecurity's playbook and scales testing. *Common thread*: The best solutions pair hard metrics with standing institutions, and make compliance cheaper than evasion.