The Governance Gap in AI

As organizations rush to integrate artificial intelligence into every facet of operations, AI governance has taken center stage. However, while many enterprises focus on establishing robust infrastructures—such as policy engines, model registries, and validation frameworks—they often overlook a crucial ingredient: context.

Context defines not only how AI systems operate but why, where, and for whom. It's the lens through which infrastructure decisions gain ethical, legal, and business relevance.

This blog post examines why AI governance must start with a thorough understanding of context, not just compliance structures, and how organizations can integrate contextual awareness into their AI lifecycle.

1. What Do We Mean by “Context” in AI Governance?

Context in AI governance isn’t background noise—it’s the foundation for creating meaningful, responsible oversight. It defines how and why governance rules should apply in specific scenarios where AI is developed and used.

Key Aspects of Context:

• Purpose of the System: AI built for fraud detection differs significantly from one used in mental health diagnostics. Governance must align with the system’s intended use.
• Impacted Stakeholders: Who is affected—customers, employees, or vulnerable groups? Governance should prioritize fairness and protections for those most impacted.
• Deployment Setting: The environment shapes risk. A facial recognition tool in a retail store vs. an airport demands different safeguards.
• Regulatory and Cultural Norms: Governance must consider local laws and cultural expectations, from GDPR compliance to ethical norms on surveillance.
• Risk Level: Not all AI systems carry equal risk. High-stakes use cases, such as healthcare or criminal justice, require more rigorous oversight.

2. Why Infrastructure Alone Falls Short

Strong infrastructure is vital to AI governance. Tools such as model cards, bias audits, data lineage catalogs, and real-time monitoring dashboards help organizations track and manage their AI systems effectively. They bring transparency, repeatability, and structure to an otherwise complex and fast-moving field.

But infrastructure is not the same as judgment. These tools can capture performance metrics and surface anomalies, but they don’t ask the more profound questions. They don’t decide whether a system should be built in the first place, or whether it's appropriate for the people or environments it impacts. That role still belongs to humans, and humans need context.

Why Infrastructure Needs Contextual Oversight

Even the most advanced AI infrastructure can fall short without human-centered, context-aware decision-making. Here’s why:

• Ethical Blind Spots:
  Tools can flag statistical bias but cannot assess broader ethical harms. For instance, a model might achieve demographic parity but still reinforce stereotypes or perpetuate exclusion in subtle ways. Without understanding the lived realities of affected groups, these issues go undetected.
• Legal Misalignment:
  Compliance dashboards might confirm that data was collected with consent, but that doesn’t guarantee alignment with nuanced regulatory obligations like GDPR’s “right to explanation” or HIPAA’s rules on data minimization. These require a legal-context lens, not just infrastructure checks.
• Operational Misfit:
  A model might perform well in a controlled test environment but behave unpredictably in real-world scenarios. Infrastructure can measure this, but only humans can assess whether it's still fit for purpose, especially when it impacts critical services like healthcare, hiring, or financial lending.

Context Gaps in Hiring Algorithms

Consider a hiring algorithm designed to screen resumes. It might pass gender fairness tests with flying colors. On paper, the tool is compliant, explainable, and monitored. But in practice, it could still filter out neurodiverse candidates who express their skills differently or have non-linear career paths.

Why does this happen? Because the governance framework didn’t include inclusion context, it focused on technical parity metrics without questioning whether the design assumptions themselves excluded valuable talent. Infrastructure caught no error, but harm still occurred.

3. Building Context-Aware AI Governance

To move from checkbox compliance to truly responsible AI, governance systems must be designed with context at their core, not as an afterthought. This means recognizing that each use case of AI exists within a unique ecosystem of risks, norms, stakeholders, and impacts. A governance framework that ignores this reality risks being blind to harm, ineffective in oversight, and disconnected from the real world.

Here’s how organizations can embed contextual intelligence into their AI governance strategies:

A. Contextual Risk Assessment Frameworks

Traditional risk matrices often fail to capture the domain-specific nuances of AI deployment. Instead of relying on generic labels like "high," "medium," or "low" risk, organizations should adopt sector-specific assessments that reflect both the technical and societal dimensions of risk, providing a more nuanced understanding.

Examples by Domain:

• Healthcare:
  Prioritize explainability, clinical validation, and human-in-the-loop design. Misdiagnosis due to black-box outputs can have life-threatening consequences. The governance process must reflect the high-stakes nature of care delivery.
• Finance:
  Emphasize bias prevention, regulatory alignment, and audit trails to ensure effective data management. Decisions regarding credit scoring or fraud detection must be transparent, fair, and in compliance with relevant financial regulations, such as the Equal Credit Opportunity Act or Basel III.
• Education:
  Focus on fairness across demographic groups, transparency in grading or admissions algorithms, and accessibility. A tool that rates students based on online behavior may penalize those from different socio-economic or neurodiverse backgrounds.

Beyond Model Risk:

Governance should not only assess model performance risks (e.g., false positives/negatives) but also societal risks, such as reinforcing systemic inequality or undermining trust in institutions. This dual-lens approach ensures governance is holistic, not just technical.

B. Stakeholder-Centric Design

AI systems are rarely used in isolation—they impact people, communities, and public trust. That’s why governance must include the voices of those affected, not just those building the technology.

Key Participants:

• Policy & Legal Teams:
  Ensure alignment with regulations and assess long-term policy impacts.
• Domain Experts:
  Offer crucial insight into context-specific expectations, workflows, and failure modes. For instance, a radiologist’s input is vital when designing AI for medical imaging.
• Impacted Users:
  Whether employees being monitored or citizens subject to surveillance, users can surface risks that may not be visible in the design room.
• Ethics & Civil Society Organizations:
  These partners provide critical, independent perspectives that can highlight social risks, power imbalances, and equity concerns.

When governance is inclusive, it is more likely to be resilient, ethical, and context-aware.

C. Context-First Documentation

Governance documentation—like model cards, system datasheets, and impact assessments—should not be treated as post-hoc formalities. Instead, they should begin with, and be shaped by, a deep understanding of the use case and its ecosystem.

What to Include from the Start:

• Intended Use Case:
  Clearly define the specific goal, deployment setting, and operational scope of the system. Avoid vague or overly broad applications.
• Known Risks and Domain Challenges:
  Document what is already known to be risky in this field. For instance, facial recognition in public spaces has a history of racial bias—this context should shape testing and deployment strategies.
• Underlying Assumptions:
  What assumptions were made about data, behavior, or environment? Assumptions often introduce hidden risks when moved to new or uncontrolled settings.

Living Documentation:

Importantly, documentation should not be static. As systems are retrained, redeployed, or updated, their governance documentation should be updated accordingly. A “living” document reflects the dynamic nature of both AI systems and the real-world contexts they interact with.

4. Operationalizing Context at Scale

As organizations deploy AI across departments and geographies, the idea of tailoring governance to each system’s context might seem overwhelming. How can enterprises maintain contextual awareness when managing hundreds or thousands of AI models? The answer lies in embedding contextual governance into workflows, tools, and policies from the ground up- making it repeatable, structured, and automated where possible.

Here’s how enterprises can bring context-aware governance to scale without slowing innovation:

A. Create AI Use Case Registries with Contextual Tags

A centralized AI use case registry serves as a living catalog of all AI/ML projects within the organization. But more than just an inventory, it should include contextual metadata that informs risk and oversight.

Key Metadata Fields to Include:

• Industry or Domain: (e.g., healthcare, logistics, HR)
• Risk/Impact Level: (low, medium, high – based on business criticality or human impact)
• Jurisdiction: (country/state-specific legal obligations)
• Vulnerable Populations Affected: (children, patients, job applicants, marginalized groups)

This structured tagging allows organizations to automatically align AI projects with appropriate governance policies, such as:

• Triggering additional audits for healthcare use cases
• Assigning specific legal teams based on deployment geography
• Flagging models with high-risk human impact for ethics board review

By linking use cases to contextual governance policies, enterprises avoid a one-size-fits-all model and instead adopt adaptive, dynamic oversight.

B. Integrate Context into MLOps Pipelines

Most modern organizations rely on MLOps (Machine Learning Operations) to streamline model development, testing, deployment, and monitoring. The MLOps pipeline is an ideal point of integration for governance, but it must go beyond technical validation to include contextual checkpoints.

Where to Embed Context:

• Model Training Phase:
  Trigger a fairness or explainability review if the use case involves sensitive features (e.g., race, gender).
• Pre-Deployment Approval:
  Require sign-off from legal, compliance, or ethics teams if the use case is flagged as high-impact or operates in a regulated sector.
• Drift & Post-Deployment Monitoring:
  Set contextual thresholds. For example, if a sentiment classifier in a customer service tool starts misclassifying 20% of queries in a new language, flag it for review.

Example in Action:

Imagine a financial services chatbot originally built for U.S. customers is being launched in Southeast Asia. The pipeline should automatically detect the change in geography, triggering:

• A legal review based on local consumer protection laws
• Localization checks for cultural appropriateness
• A reassessment of training data to ensure relevance and fairness

With these contextual triggers built into MLOps, governance becomes proactive rather than reactive.

C. Leverage Contextual Governance Playbooks

One of the most scalable approaches to embedding context is through pre-built decision frameworks or playbooks. These documents help teams across the organization consistently navigate governance decisions, even when specialists aren't available.

What Playbooks Should Include:

• AI Scenario Templates: Tailored guidelines for common use cases (e.g., biometric surveillance, employee productivity tracking, AI-generated content).
• Risk Escalation Paths: Clear thresholds for when to escalate issues to legal, ethics boards, or executive teams.
• Incident Response Checklists: Based on real-world AI failures—e.g., discriminatory hiring, hallucinations in healthcare chatbots—to inform future risk mitigation.

These playbooks should be living documents, continuously updated with:

• New regulatory developments (like the EU AI Act or U.S. state laws)
• Insights from internal incident reviews
• Feedback from users and domain experts

By making these resources widely accessible and regularly updated, organizations can democratize contextual governance and reduce over-reliance on centralized compliance teams.

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5. Regulations Are Catching Up - So Should You

As artificial intelligence becomes a foundational layer in critical systems—from hiring and healthcare to finance and law enforcement—governments and regulatory bodies worldwide are moving swiftly to ensure it is used responsibly. And at the heart of these regulations is a common theme: context matters.

No longer is it sufficient to demonstrate that an AI system was technically correct or followed standardized practices. Regulators now expect organizations to prove that AI systems are appropriate for the specific use case, risk level, and societal impact, and that governance frameworks are tailored accordingly.

Let’s look at how leading regulations and frameworks are formalizing the role of context in AI governance:

A. The EU AI Act: Contextual Risk Classification

The European Union’s AI Act, the most comprehensive legal framework for AI to date, takes a risk-based approach that directly integrates context into legal obligations.

• Unacceptable Risk: AI use cases that manipulate behavior (e.g., social scoring systems) or violate fundamental rights are banned outright.
• High Risk: Systems used in critical sectors—like biometric identification, education scoring, or creditworthiness—must adhere to strict transparency, documentation, and human oversight requirements.
• Limited and Minimal Risk: Applications like spam filters or AI-driven recommendations are subject to lighter obligations but still require disclosure in some cases.

What makes the EU AI Act unique is that compliance depends on what the system does, where it’s used, and who it affects. This means that the same underlying model might be treated very differently depending on whether it’s used for gaming recommendations or evaluating job candidates.

Bottom line: If your organization doesn’t document and act on contextual risk levels now, it could face non-compliance, fines, or product bans in the future.

B. OECD Principles on AI: Human-Centric, Context-Aware AI

The OECD Principles on Artificial Intelligence, adopted by 46 countries, call for AI systems that are:

• Inclusive and sustainable
• Respectful of the rule of law and democratic values
• Transparent and explainable
• Accountable through appropriate governance mechanisms

But crucially, these principles advocate that AI governance should be tailored to the system’s impact and context, including:

• The environment in which the system operates
• The populations it affects
• The ethical considerations specific to each application

For example, an AI used for educational grading must consider the fairness and long-term opportunities for students. In contrast, one used for optimizing warehouse logistics would focus more on labor and safety.

C. NIST AI Risk Management Framework: Scenario-Specific Risk Assessment

In the United States, the National Institute of Standards and Technology (NIST) has released an AI Risk Management Framework that emphasizes the importance of application-specific assessments.

The NIST framework urges organizations to:

• Characterize the socio-technical context of AI applications
• Conduct impact assessments across different lifecycle stages
• Continuously monitor for emerging risks as systems evolve or shift use

This means organizations should not only measure technical robustness but also map potential downstream harms, legal consequences, and trust dynamics that emerge from the AI’s role in the real world.

D. Why Context-Driven Compliance Builds Trust

Beyond compliance, embedding context into your AI governance strategy is a competitive differentiator.

• Regulators will demand it. By aligning early, companies can reduce regulatory friction and avoid retroactive fixes.
• Users expect it. People are more willing to adopt AI systems when they know those systems are governed in ways that respect their rights, roles, and realities.
• Trust compounds. Systems designed with ethical nuance and contextual awareness are more likely to perform reliably, meet social expectations, and retain long-term user confidence.

In short, context is no longer a “nice to have”—it’s a legal and ethical imperative.

Build for Context, Govern for Trust

AI governance isn’t a fixed set of tools or policies—it’s a dynamic, evolving system shaped by the context in which AI operates. While infrastructure, such as model audits, data sheets, and compliance workflows, is essential, it is only meaningful when grounded in real-world factors: who the system impacts, what decisions it influences, and the legal or cultural landscape it enters. Governance must go beyond technical correctness to consider ethical relevance, social risk, and public trust.

Embedding context throughout the AI lifecycle—design, deployment, and monitoring—ensures that systems are not only compliant but also responsible. It aligns governance with human values, diverse stakeholder needs, and the unique conditions of each use case. In a world where AI decisions increasingly affect lives, trust isn’t earned through checkboxes—it’s built through contextual intelligence.

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