Accelerated Innovation

Using Model and Risk Cards to Improve GenAI Transparency

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Using Model and Risk Cards to Improve GenAI Transparency

Description

This capability focuses on implementing Model Cards, System Cards, and Risk Cards to improve transparency, accountability, and safe usage of GenAI systems. These documentation artifacts provide clear, standardized disclosures on model behavior, capabilities, limitations, risks, and usage policies.

Why it's Important

GenAI models can behave unpredictably or produce unintended results if used without sufficient understanding of their design and limitations. Transparency tools like Model and Risk Cards help internal teams, end users, and regulators make informed decisions about GenAI deployment. They also reinforce responsible AI practices by ensuring the technology is explained, evaluated, and governed in line with organizational values and stakeholder expectations.

Why it's Challenging @ Scale

  • No consistent format across teams: Model and Risk Cards vary widely in structure and depth, leading to confusion or lack of trust.
  • Difficult to keep cards current: Rapid model updates often outpace documentation, leaving users with outdated transparency data.
  • Limited stakeholder engagement: Key contributors like security, compliance, and end users are often left out of the authoring process.
  • Gaps in risk identification: Without strong prompting, teams may overlook edge cases, misuse scenarios, or unintended consequences.
  • Transparency is not prioritized: Model performance and feature delivery often take precedence over documentation and explainability.

Complexity

High: This capability requires standardized documentation frameworks, interdisciplinary collaboration, validation workflows, and cultural reinforcement of transparency as a non-negotiable GenAI quality.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

The most important part of any journey is starting… To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the Securing Your GenAI Solution workshop (2 hrs.) to understand foundational key concepts and explore applied best practices.
  • Introducing GenAI Threat Models and Security Posture
  • Understanding Attack Surfaces in GenAI Workflows
  • Establishing Basic Security Principles for LLMs
  • Identifying Security Stakeholders and Roles
  • Aligning Security with Compliance Requirements
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy.
  • Align on your Current State and define your Target State
  • Create an actionable enablement plan
  • Define target timeline and measures of success
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Draft a Model Card Template: Start with a basic structure that includes purpose, training data, intended use cases, and known limitations.
  • Conduct a Pilot Risk Card Review: Choose one GenAI system and assess risks using a lightweight framework to uncover gaps.
  • Align on Required Transparency Fields: Collaborate across teams to agree on a minimum set of attributes for disclosure.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • A Deep Dive into GenAi Solution Threat Modeling
  • A Deep Dive into Enterprise Access Control for GenAI Solutions
  • A Deep Dive into Preventing Prompt Injection Attacks
  • A Deep Dive into Preventing Insecure Output Handling
  • A Deep Dive into Preventing Data Poisoning
  • A Deep Dive into Preventing Denial of Service
  • A Deep Dive into Preventing GenAI Supply Chain Risks
  • A Deep Dive into Preventing Sensitive Information Disclosure
  • A Deep Dive into Preventing Insecure GenAI Solution Plugins
  • A Deep Dive into Preventing Excessive LLM Agency
  • A Deep Dive into Preventing LLM Overreliance
  • A Deep Dive into Preventing GenAI Model Theft
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Evaluate whether current Model and Risk Card drafts cover relevant risks, usage contexts, and stakeholder needs.
  • Define in-scope Processes and Guardrails: Establish documentation standards for how and when model cards must be authored, reviewed, and updated.
  • Close any Data or Measurement Gaps: Capture where transparency is lacking and identify dependencies (e.g., model lineage or training data) to close those gaps.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Begin with high-impact GenAI systems and gradually scale transparency documentation across all models.
  • Build Awareness and Finalize Enablers: Train teams on how to interpret and use Model and Risk Cards in their workflows.
  • Operationalize Your Comms Plan: Share updates and success stories about how transparency tools are being adopted across the organization.
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Create a Centralized Template Library: Develop reusable templates for Model, System, and Risk Cards that include required fields and example language.
  • Establish Review and Approval Workflows: Implement governance processes for validating transparency artifacts before model deployment.
  • Assign Card Ownership Roles: Clarify which teams (e.g., product, legal, data science) are responsible for completing and maintaining each section.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Incorporate Transparency in GenAI Onboarding: Make Risk and Model Cards a required part of solution enablement and user education.
  • Track Card Usage and Impact: Monitor how often cards are accessed or referenced in decision-making to measure their value.
  • Extend Coverage to Vendor or Open Models: Ensure transparency standards are applied to external models integrated into your GenAI ecosystem.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Showcase Cards That Improve Trust: Highlight examples where Model or Risk Cards helped clarify model capabilities or limitations for key stakeholders.
  • Recognize Documentation Leaders: Spotlight teams or individuals who contributed to creating high-impact or widely used transparency artifacts.
  • Share Metrics That Demonstrate Value: Report on adoption, completion rates, or stakeholder satisfaction with transparency tooling.
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific Capability represents a clear competitive advantage for your business.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Embed Model and Risk Cards into Toolchains: Ensure cards are available in development, review, and launch workflows for every GenAI system.
  • Automate Card Generation Where Possible: Leverage templates, structured data, or metadata to pre-populate transparency content.
  • Integrate Cards into Access and Risk Reviews: Make review of transparency documentation a prerequisite for model approvals or user access.
  • Leverage Automation: Use GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Use AI to Summarize Technical Details: Deploy GenAI to help translate engineering documentation into plain language card content.
  • Pre-fill Known Attributes via APIs: Automate entry of model IDs, versioning, or training specs from system registries into documentation.
  • Flag Incomplete or Outdated Cards: Use automated checks to identify missing content or cards that need revision after updates.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Expand Card Coverage to Multimodal and Agentic Systems: Extend documentation to cover audio, video, image, and agent-based GenAI tools.
  • Benchmark Against Regulatory or Industry Standards: Align your transparency practices with NIST, ISO, or local compliance frameworks.
  • Analyze Card Usage Analytics for Improvement: Track engagement with cards to understand what users value or ignore, and evolve accordingly.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Treating transparency as a one-time task: Model and Risk Cards must be living documents that evolve with system changes.
  • Underestimating the time required: Creating effective documentation involves cross-functional collaboration and iteration.
  • Focusing only on technical users: Cards should be readable and useful for non-technical stakeholders, including end users and executives.
  • Skipping pilot reviews: Rolling out without testing cards for clarity, completeness, and usability leads to limited adoption.
  • Allowing documentation gaps to persist: Incomplete or missing cards undermine trust and raise compliance risks.

Targeted Benefits

While Using Model and Risk Cards to Improve GenAI Transparency can be challenging, its benefits are clear and compelling, including:

  • Increased user confidence and adoption: Clear, accessible transparency materials help users understand what models can and can’t do.
  • Faster legal and compliance reviews: Pre-documented risks, intended uses, and mitigation strategies accelerate regulatory approvals.
  • Stronger alignment with Responsible AI principles: Transparency tools help reinforce fairness, accountability, and explainability standards.
  • Better decision-making by business users: Cards empower teams to choose the right model or tool based on documented capabilities and limitations.
  • Greater differentiation in competitive environments: Transparency becomes a trust signal that distinguishes your solutions in the market.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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