The Mitigating ‘Hidden’ AI Risks Toolkit

⚡ Quick Summary

This toolkit from the Global Partnership on AI (GPAI) tackles what often slips through the cracks in AI risk assessments: systemic, low-signal, and underreported risks. It focuses on the “hidden” harms of AI—ranging from low-stakes misuse in workplace tools to cumulative discrimination in opaque systems. Structured into a six-step mitigation cycle, the toolkit helps policymakers, civil society, and implementers assess overlooked risks using concrete prompts, sectoral case studies, and power-mapping exercises. The core value: it moves beyond checklists toward reflective, participatory governance methods that surface invisible risks before they entrench. The toolkit is designed for adaptation—not prescription—making it especially useful for under-resourced or rapidly evolving contexts.

🧩 What’s Covered

The document is structured into four main sections, wrapped around a six-phase mitigation cycle that guides users through the process of identifying, understanding, and responding to hidden AI risks.

1. Defining ‘Hidden’ AI RisksIt categorizes hidden risks into three types:Each category is illustrated with examples drawn from education, employment, justice, and consumer tech.
   • Under-the-Radar Use Cases (e.g. recruitment filters, classroom surveillance)
   • Low-Visibility Impacts (e.g. cognitive overreach, procedural bias)
   • Slow-Burn Harms (e.g. societal polarization, long-term disempowerment)
2. Six-Step Mitigation CycleThe core of the toolkit includes:For each step, the toolkit provides “Guiding Questions,” illustrative scenarios, and tables comparing mitigation strategies.
   • Identify: Tools for mapping power asymmetries, spotting low-visibility deployment
   • Understand: Questions for evaluating affected communities and systemic bias
   • Engage: Methods for meaningful stakeholder participation
   • Act: Options for regulatory, design, or market-based interventions
   • Reflect: Guidance on surfacing blind spots and institutional limitations
   • Review: Ongoing risk horizon scanning and impact audits
3. Field-Specific Use CasesThe toolkit applies its methods to five domains:These examples help ground the framework and show its utility across contexts.
   • Workplace monitoring software
   • AI-powered hiring systems
   • Online education tools
   • Predictive policing
   • Financial access scoring
4. Supporting Tools
   • A Power-Mapping Template to uncover who benefits or bears risk
   • A Design for Reflection checklist
   • Sample metrics for participatory evaluation
   • Embedded prompts for governance experimentation

💡 Why it matters?

AI governance has a visibility problem. Many harms don’t show up in high-profile failures or regulatory filings—they happen slowly, quietly, or outside the spotlight. This toolkit builds capacity to see and act on those risks. It’s especially relevant for governments, civil society, and local implementers trying to surface risks missed by industry-led standards. Its participatory framing supports a more pluralistic, equity-centered model of governance—well aligned with UNESCO, OECD, and Global South perspectives.

❓ What’s Missing

• Integration with Risk Taxonomies: Doesn’t clearly connect with existing frameworks like NIST RMF or OECD’s risk classification.
• Scalability in Complex Systems: Lacks implementation guidance for large-scale AI applications.
• Private Sector Adaptation: Designed mostly for policymakers and NGOs; less guidance for internal corporate use.

👥 Best For

• Public sector teams conducting AI impact assessments
• Civil society organizations advocating for AI transparency
• Governance researchers and policy labs
• Ethics officers and DEI teams working with localized deployment
• International organizations funding AI infrastructure projects

📄 Source Details

Title: The Mitigating ‘Hidden’ AI Risks Toolkit

Published by: GPAI Responsible AI Working Group

Date: June 2024

Length: 52 pages

Structure: Six-phase framework with case studies, guiding questions, and reflection prompts

Website: gpai.ai

📝 Thanks to

The GPAI Responsible AI Working Group, with special appreciation for contributors from Mozilla Foundation, Vidhi Centre for Legal Policy, and CIFAR, for bringing long-overdue attention to the silent edge of AI risk.