Addis Ababa University

n my view, success would be defined by three primary pillars: 1. The "Interoperability" Consensus Success is not a single global law, but a "Rosetta Stone" for regulation. If the Dialogue establishes a mechanism to align disparate frameworks—such as the EU AI Act, the US Executive Order, and China's regulations—it prevents a fragmented "splinternet." A successful outcome would be a formal agreement on mutual recognition of safety audits, allowing innovation to scale across borders without redundant compliance hurdles. 2. Meaningful Global South Inclusion Governance is often a monologue by tech-exporting nations. A breakthrough success would be the establishment of an AI Capacity & Sovereignty Fund. This would ensure that developing nations are not merely "data sources" or "consumers," but active participants with the infrastructure to govern AI according to their unique cultural and linguistic contexts. 3. Verification over Vows The era of voluntary "responsible AI" pledges must end. Success looks like a commitment to independent, third-party technical red-teaming and standardized benchmarks for "frontier models." The Litmus Test: If the Dialogue concludes with a permanent, technically-empowered International Panel on AI Safety (analogous to the IPCC for climate change), it will have succeeded. We need a body that provides a shared, evidence-based reality upon which policy can be built. Ultimately, success is moving the needle from "What should we do?" to "How do we technically enforce it together?"

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Protection and promotion of human rights
• Transparency, accountability, and human oversight

My selection of these outcomes-interoperability, Global South inclusion, and technical verification-is based on the necessity of moving AI governance from the "philosophical" stage to the "operational" stage. 1. Interoperability (The Structural Goal) Without a shared language for safety, we risk a "compliance tax" that only the largest tech giants can afford. By focusing on interoperability, we ensure that safety standards in one region are recognized in another. This promotes a competitive global market where small-to-medium enterprises can innovate without navigating a labyrinth of conflicting laws. 2. Global South Inclusion (The Ethical Goal) AI governance that excludes the developing world is not global governance-it is a digital oligarchy. True success requires that the "dialogue" isn't just about risk mitigation for wealthy nations, but about equitable access and agency. If the Dialogue addresses the digital divide, it ensures that AI becomes a tool for global development rather than a driver of further inequality. 3. Technical Verification (The Enforcement Goal) Words and voluntary pledges are insufficient for a technology that moves as fast as AI. We need a "trust but verify" model. Selecting independent red-teaming and standardized benchmarks as a success metric ensures that governance is rooted in technical reality, not just political posturing. It creates a measurable baseline for what constitutes a "safe" model. In essence: My selection prioritizes a governance model that is practical enough to work, inclusive enough to be fair, and technical enough to be safe. It shifts the focus from managing the politics of AI to managing the actual technology.

. The Energy-Governance Nexus The environmental cost of AI is a burgeoning ethical crisis. Emerging governance must integrate sustainability metrics directly into compliance frameworks. We cannot discuss "Responsible AI" without addressing the massive water and energy consumption required for training frontier models. Governance success should be tied to "Green AI" standards-favoring algorithmic efficiency over brute-force scale. 2. Cognitive Sovereignty and Content Integrity As synthetic media becomes indistinguishable from reality, the issue of cognitive sovereignty-the right of individuals to remain unmanipulated by AI-is an emerging human rights frontier. Current themes often focus on "misinformation," but the deeper issue is the degradation of the shared information ecosystem. We need global standards for cryptographic provenance (watermarking) that are enforced at the hardware level, not just as voluntary software tags. 3. AI as a Sovereign Utility There is a growing risk of "AI dependency," where sovereign nations rely on a handful of private companies for essential public services (healthcare, legal, education). A critical missing theme is the de-risking of private monopolies. Governance should explore the creation of "Public Interest AI" infrastructures-essentially a global utility model-that prevents the enclosure of the "digital commons" by commercial interests. The Blind Spot: The most significant emerging issue is the speed of obsolescence. Laws drafted today may be irrelevant by the time they are ratified. Success requires a "Dynamic Regulation" model where technical benchmarks automatically trigger policy reviews, ensuring that governance evolves at the speed of silicon.

In the Ethiopian public sector and the broader East African region, the gaps in AI governance create a rapid modernization and institutional vulnerability. Challenges: The "Regulatory Vacuum" The most significant challenge is the capacity gap in enforcing ethical compliance. As we digitize institutional frameworks (such as our work on the AAU-Integrity 2026 software), the lack of localized AI standards means we often rely on foreign, "black-box" algorithms. Sector Risk: Without domestic auditing tools, there is a risk that AI-driven anti-corruption measures could inadvertently inherit biases from training data that does not reflect Ethiopia's unique socio-political landscape. Digital Sovereignty: The region faces "data extractionism," where local data is used to train global models that are then sold back to us, with no governance over how that data is stored or repurposed. Opportunities: The "Leapfrogging" Potential Conversely, these gaps present a rare opportunity to build "Integrity by Design." Indigenous Frameworks: We have the opportunity to integrate indigenous governance wisdom, such as the Gadaa System's principles of accountability and peaceful power transfer, into modern algorithmic audits. This would create a uniquely "Ethiopian" AI ethics model. Green Ethics Synergy: In our sector, the push for AI provides a platform to champion "Green Ethics." By mandating energy-efficient AI for public institutions, Ethiopia can lead the region in sustainable digital transformation, aligning technological growth with our national environmental stewardship goals. The Bottom Line The sector is currently at a crossroads. The opportunity lies in moving beyond being a consumer of technology to becoming a developer of culturally-aligned, ethical AI solutions that prioritize institutional integrity and environmental sustainability.

The AI Dialogue serves as a critical diplomatic bridge between technical capabilities and global policy, moving the conversation from competitive posturing to collective safety. Its role in advancing international cooperation is threefold: 1. Establishing a "Global Baseline" of Safety The Dialogue can standardize the definition of "high-risk" AI. Currently, what is considered a risk in one jurisdiction may be unregulated in another. By facilitating a shared technical taxonomy, the Dialogue enables nations to cooperate on cross-border incident reporting. If a frontier model exhibits a dangerous emergent behavior, the Dialogue provides the infrastructure for "coordinated disclosure," similar to how cybersecurity vulnerabilities are managed globally today. 2. Democratizing the "Compute Divide" International cooperation often fails when the "have-nots" feel sidelined. The AI Dialogue can facilitate Resource Sharing Agreements, where leading AI nations provide subsidized "compute credits" and open-source foundation models to developing countries. This transforms AI governance from a restrictive barrier into a collaborative developmental tool, ensuring that safety standards are adopted globally because the benefits of the technology are shared equitably. 3. Creating "Regulatory Sandboxes" for Global Challenges The Dialogue can coordinate international policy experiments. By establishing joint "sandboxes," countries can co-develop AI solutions for transboundary issues like climate modeling, pandemic prediction, and hydro-politics. This shifts the focus of cooperation from preventing harm to active problem-solving, building trust through shared technical successes. The Ultimate Role: A Permanent Secretariat The most vital role for the Dialogue is to evolve into a permanent, inclusive body—a "UN for AI"—that provides a continuous forum for negotiation. This prevents "governance lag," ensuring that international law evolves as quickly as the underlying neural networks, maintaining a stable and predictable global digital order.

The AI Dialogue should avoid "reinventing the wheel" by strategically anchoring itself to existing frameworks while providing the connective tissue they currently lack. Strategic Connections The G7 Hiroshima AI Process & EU AI Act: These provide the most mature "risk-based" taxonomies. The Dialogue should build upon these definitions to create a broader international standard, ensuring that "high-risk" classification is consistent globally. The UN Global Digital Compact: This serves as the primary political vehicle for digital equity. Connecting with it ensures that AI governance remains a pillar of the Sustainable Development Goals (SDGs). The GPAI (Global Partnership on AI): While GPAI excels at academic and technical research, it often lacks the multi-stakeholder "diplomatic teeth" to translate research into policy. The Added Value of the AI Dialogue The Dialogue's unique value lies in its inclusive agility. Where existing bodies are often either too exclusive (G7) or too slow (formal UN treaties), the Dialogue can act as: A "Gap-Filler" for Interoperability: Its primary added value is creating a "Model Law" or a mutual recognition framework. This allows a company certified safe in Ethiopia or the African Union to operate in the EU or US markets without redundant, costly re-auditing. A Technical-to-Political Translator: It can serve as the clearinghouse where frontier laboratory research (e.g., from OpenAI or Anthropic) is distilled into actionable safety benchmarks for non-technical policymakers. The "Global South" Veto: By design, it can provide a formal mechanism for developing nations to challenge "digital colonialism," ensuring that international cooperation includes enforceable commitments to infrastructure and knowledge transfer, not just safety restrictions.

For the AI Dialogue to transcend typical diplomatic "talk shops," it must adopt a tiered, multi-stakeholder architecture that balances expert technical rigor with broad democratic legitimacy. Stakeholder Contributions Academia & Think Tanks: Should act as the "Technical Truth Layer," providing independent, peer-reviewed audits of frontier models to counter industry-led safety claims. Private Sector: Developers must provide "Safe Harbor" access to their model weights for vetted researchers and commit to a "Governance-by-Design" framework. Civil Society & Labor: Must serve as "Impact Monitors," documenting real-world harms in marginalized communities and advocating for algorithmic labor rights. National Governments: Their role is to provide the legal enforcement mechanisms that turn the Dialogue's consensus into binding domestic policy. Recommendations for Format and Structure The "Sprints & Summits" Cadence: Instead of one annual meeting, the Dialogue should operate via quarterly "Technical Sprints"—working groups focused on specific issues like watermarking or energy use—culminating in a high-level political summit to ratify technical findings. Hybrid "Plug-and-Play" Working Groups: The structure should be modular. For example, a working group on "AI in Healthcare" would "plug in" medical ethics experts and patient advocacy groups, rather than relying on generalist tech regulators. The "Citizen's Assembly" Component: To ensure democratic legitimacy, the Dialogue should incorporate a Deliberative Citizen Panel—randomly selected individuals from across the globe who review and provide feedback on proposed governance frameworks. The Output Mechanism Every session must conclude with a "Technical White Paper" and a corresponding "Policy Roadmap," ensuring that the transition from technical reality to legislative action is seamless and evidence-based.

The most glaring omission in global AI governance is the "End-User at the Periphery." While developers and regulators in tech hubs dominate the conversation, the following groups remain structurally sidelined: 1. Indigenous and Non-Western Knowledge Systems Governance frameworks are heavily rooted in Western individualistic ethics. Voices from communities practicing communal or indigenous governance—such as the Gadaa system in East Africa—are rarely consulted. These perspectives offer vital alternatives for collective accountability and long-term stewardship that contrast with the short-term market focus of Silicon Valley. 2. Labor in the "Human-in-the-Loop" Supply Chain The millions of workers in the Global South performing data labeling, content moderation, and RLHF (Reinforcement Learning from Human Feedback) are the invisible backbone of AI. They bear the brunt of psychological trauma and economic precarity, yet they have no seat at the policy table to discuss labor standards or algorithmic management. 3. Linguistic and Cultural Minorities AI is being governed in a handful of high-resource languages. Communities speaking low-resource languages are excluded from safety testing, leading to models that may be "safe" in English but harmful or biased in their native tongues. Strategies for Radical Inclusion: The "Regional Secretariat" Model: Instead of centralized summits in Geneva or New York, the AI Dialogue should establish regional hubs that gather input in local languages and cultural contexts before global consolidation. Labor Representation Mandates: Policy bodies must include representatives from digital labor unions and civil society organizations from the Global South, moving beyond "industry-only" working groups. Inclusive Red-Teaming: Incentivize "community-led red-teaming," where local populations are funded to test models for cultural nuances and biases specific to their region before a model is granted a "global" safety certification. True inclusion isn't just about an invitation to the room; it's about redefining the room's architecture to value diverse ways of knowing.

o move beyond static panels and scripted interventions, the AI Dialogue should employ formats that prioritize asynchronous collaboration, technical transparency, and deliberative depth. 1. The "Red-Teaming" Arena Instead of theoretical debates, host live "Policy Red-Teaming" sessions. Regulators, civil society leaders, and developers would be presented with a specific AI-driven crisis scenario (e.g., a cross-border deepfake impacting an election). They must collaborate in real-time to draft a joint response protocol, exposing gaps in current governance faster than any white paper. 2. Algorithmic Impact "Show & Tells" Replace traditional slide decks with Interactive Sandboxes. Developers should be required to demonstrate their models' behavior within a controlled environment, allowing stakeholders to "stress-test" the AI against specific cultural or ethical edge cases. This shifts the engagement from "telling" to "showing," making abstract risks tangible for non-technical policymakers. 3. Asynchronous "Global Wikis" for Policy Governance should be a "living document." Implementing an Open-Source Policy Ledger allows stakeholders from different time zones—especially in the Global South—to contribute, comment, and "fork" regulatory drafts. This ensures that a participant in Addis Ababa has the same agency to edit a proposal as one in Brussels, democratizing the drafting process. 4. The "Inverse Panel" (Youth & Minority-Led) In this format, the traditional hierarchy is flipped: senior policy officials and tech CEOs sit in the audience while youth leaders, gig workers, and indigenous scholars take the stage to set the agenda. The officials are then tasked with responding to the specific challenges posed, ensuring that those most impacted by AI are the ones steering the dialogue. 5. Deliberative Polling & Real-time Synthesis Utilize AI-driven synthesis tools to map areas of consensus and "productive friction" during the event. This prevents the dialogue from circling the same points and provides a real-time visual map of the global collective intent.

1. Risk-Based Regulatory Frameworks The EU AI Act: The first comprehensive legal framework to categorize AI systems by risk (Unacceptable, High, Limited, Minimal). It mandates strict transparency and data quality requirements for "High-Risk" systems, such as those used in recruitment or law enforcement. Canada's Algorithmic Impact Assessment (AIA): A mandatory tool for federal institutions to assess the ethical and legal risks of automated decision-making systems before they are deployed. 2. Standardized Transparency & Documentation Model Cards for Model Reporting: A practice (pioneered by Google and others) where developers publish a "nutrition label" for AI models, detailing their training data, intended use, and known biases. Data Sheets for Datasets: Standardized documentation for the datasets used to train AI, identifying potential skews or ethical concerns in the source material. 3. Collaborative Safety & Auditing Platforms The MLCommons: An open engineering consortium that creates standardized benchmarks to measure the speed, accuracy, and safety of AI systems across the industry. NIST AI Risk Management Framework (RMF): A voluntary but highly influential U.S. framework that provides a structured process to "Govern, Map, Measure, and Manage" AI risks throughout the system lifecycle. 4. Inclusive Governance Approaches The African Union's AI Continental Strategy: An initiative focused on "Digital Sovereignty," ensuring AI development aligns with the African Charter on Human and Peoples' Rights while promoting local language inclusion. Deliberative Citizen Assemblies: Used by organizations like the Ada Lovelace Institute to incorporate public values into tech policy through deep, evidence-based public consultation. These examples illustrate that successful governance is a combination of legislative mandates, technical standards, and inclusive participation.