The federal government stands at a pivotal juncture in adopting advanced AI systems. While the potential benefits of AI in government are immense–from enhancing cybersecurity to streamlining logistics and improving citizen services–these applications often involve sensitive data and high-stakes decisions. Such systems also demand the highest levels of transparency and accountability.
Explainable AI (XAI) refers to methods and techniques that allow human users to understand and trust the results and output created by machine learning algorithms. In government contexts, this is not merely a technical nice-to-have but a critical requirement that underpins all responsible AI adoption.
The Hard Truth About AI Hallucinations
One of the biggest misconceptions about AI is that accuracy is primarily a prompting problem. OpenAI’s recent Simple QA benchmark found that even the most advanced AI models hallucinate between 48 and 90 percent when answering simple, factual questions. For high-reliability applications where accuracy is essential, these statistics completely disqualify AI models from being used. Ask yourself, which part of your business–or which critical government service–could tolerate incorrect answers the majority of the time?
The fundamental problem isn’t in the prompting techniques or reasoning methods but rather the architectural foundation of the model. Regardless of size or training, Traditional (large language models) LLMs operate on statistical prediction rather than verifiable knowledge structures. They’re designed to minimize uncertainty in their responses, not to deliver factual accuracy.
Therefore, the solution to hallucinations is not better prompt engineering, it is better architecture. Connecting data through intelligent, persistent knowledge structures reduces hallucinations and enables a new generation of enterprise AI that is inherently trustworthy.
The Explainability Challenge in Government AI
Government agencies face unique challenges when implementing AI that demands higher standards of explainability:
- High-Stakes Decision-Making
AI often supports the government’s decisions and legislation, which could have significant impacts on individuals and communities. Whether they’re used for: determining benefit eligibility, managing critical infrastructure, or supporting defense systems, these applications require highly accurate results and complete transparency about how those results were derived.
In scenarios such as contested logistics, border security, or cyber operations, an AI system that is 80 or 90 percent accurate is still not sufficient. Such use cases require at or near 99 percent accuracy plus the ability to measure, validate, and continuously improve performance in real operational conditions.
- Legal and Regulatory Requirements
Government agencies operate within strict legal frameworks that often mandate XAI. AI systems must be able to demonstrate compliance with regulations around fairness, non-discrimination, and due process. If an agency cannot explain how its AI reached a particular conclusion, it may face legal challenges that undermine public trust and hinder innovation.
- Data Fragmentation Across Agencies
Many agencies manage hundreds–or thousands–of separate information systems, each with its own proprietary data model. Over time, the build-up of data systems leads to massive duplication of records and conflicting metadata standards making it exceedingly difficult for executives to trace AI outputs back to its source data, a fundamental requirement for explainability.
- Security and Classified Information
In national security contexts, AI systems often process classified information across several secure domains. These systems must not only protect sensitive data but also provide clear explanations of their reasoning without compromising classified sources and methods.
Core Challenges in High-Reliability Sectors
We define “high-reliability sectors” as any industry with little-to-no tolerance for error and where the consequences of rare errors could result in an outsized negative impact. Government is one of these sectors, as it also overlaps with many others, including energy, healthcare, and security. When working across these sectors, we encountered many challenges in successfully deploying AI.
- The Infrastructure Reality Gap
One of the first challenges we encountered is the ‘infrastructure reality gap,’ which is the chasm between potential AI capabilities and the reality of what can be deployed in these environments. While the industry talks about artificial general intelligence (AGI), most organizations still struggle with fundamentally disconnected information ecosystems that wouldn’t support basic machine learning or AI deployments.
- The Zero-Error Tolerance Paradox
Organizations in high-reliability sectors often face a paradox: they potentially have the most to gain from AI, yet they have the least tolerance for the uncertainty that comes with traditional AI approaches. They simply cannot justify deploying AI technology when the hallucination rates of 50 to 80 percent are seen in conventional systems. Even 10 to 20 percent error rates in optimized systems using traditional retrieval augmented generation (RAG) would disqualify AI use in these situations.
- Security and Compliance Requirements
High-reliability sectors operate under stringent regulatory frameworks and security protocols that make traditional cloud-based AI solutions non-starters. Every AI implementation in these organizations must satisfy zero-trust security requirements while also maintaining complete data sovereignty.
Knowledge Graphs: The Foundation for Truly Explainable AI
Knowledge graphs have emerged as a promising foundation for building explainable AI systems. They represent information as nodes (entities) and edges (relationships) in a flexible, interconnected structure that attempts to mirror how humans understand complex domains, without the opportunity costs.
For government agencies that deal with diverse data types (e.g., structured databases, unstructured documents, geospatial information, and streaming sensor data), this heterogeneous data can be a headache.
Knowledge graphs unify disparate data sources into a cohesive whole, preserving the relationships between entities across domains. These solutions ensure systems are connected and defined with complete transparency and traceability, unlike traditional AI models, which can struggle with complex relationships. These models see disconnected rows and columns, which can cause the AI not to notice critical connections between the data sets it uses.
The AI outputs that knowledge graphs produce are trustworthy and explainable because a human can see the connections between their nodes and edges.
Traceability Through Data Provenance
Knowledge graphs maintain data provenance (i.e., the origin of data and its usage in a model or an organization) by tracking where information came from, when it was added, and how it has been changed.
This is critical to creating explainable AI because it allows users to trace any output back to its source data. Without it, outputs could carry over “hallucinations” or other errors. This can create compliance risks for companies or allow errors to become baked into future applications.
Hypothetically, an AI system could flag a potential security threat to analysts. The analysts then look into the security threat. To ensure the system did not flag a threat incorrectly, which could either hide a real attack or cause the team to waste resources unnecessarily, the analysts will also need to verify that the AI system was correct or find what went wrong to fix it.
Excellent data provenance means that every piece of information in the graph is tagged with metadata about its source, reliability, and security classification. This ensures complete transparency, allowing the analysts to verify and follow the exact chain of evidence through the knowledge graph that led to the system flagging that threat. Without it, they are in the dark.
That is not to suggest that disconnected “black box” networks are untrustworthy, per se. Some of the most effective explainable AI systems use hybrid approaches that combine the pattern recognition power of neural networks with the transparent reasoning of knowledge graphs. The graph structure itself makes a connection between complex statistical models and human users by its very structure. This ensures that AI can provide step-by-step explanations of its logic that humans can read and comprehend.
Best Practices for Implementing Explainable AI in Government
Based on emerging patterns from successful government AI initiatives, we identify five best practices to guide the development of explainable AI systems beyond just government.
- Design for Explainability from the Start
Explainability should not be an afterthought or add-on feature. Instead, it should be designed into the architecture from the beginning. Knowledge graphs or other transparent reasoning mechanisms can be an effective core component of this architecture.
- Establish Clear Standards for Explainability
But what is explainability? Agencies have to develop clear, measurable standards for what constitutes as “explainable.” With missions as diverse as wildlife and forestry management to Medicare and Medicaid, these standards can vary between agencies. They should, however, include requirements for:
- Tracing outputs back to the source data
- Documenting the chain of reasoning
- Quantifying uncertainty in predictions
- Identifying potential biases or limitations
- Implement Multi-Level Explanations
Different audiences require different levels of explanation. A software engineer and a policy advisor both need insights into an AI’s reasoning process, but the technical complexity and presentation of those insights may differ to ensure the decision-making process is clearly communicated. People can struggle with tailoring their message to different audiences, so too do AI systems. To be effective, explainable AI must overcome this.
- Conduct Regular Audits
As AI systems evolve and ingest new data, their behavior can change. This can be subtle or, if left alone too long, can drift into more significant behavioral shifts that could even impact its usefulness. Engineers and users need to undergo regular audits to ensure that any drift in their reasoning can be detected and addressed.
- Train Personnel
Agencies should invest in training programs that build AI literacy among personnel who will be using or overseeing AI systems. This ensures that the people interacting with AI systems have a sufficient understanding to interpret explanations.
Conclusion: Transparency as a Foundation for Trust
As government agencies and the private sector adopt AI to streamline their missions, they need to ensure they are doing so effectively. Explainability is not an option, or else it can undermine public trust and operational effectiveness. Knowledge graph technologies and ensuring these systems are designed with transparency at their core, deployers and developers can guarantee that AI is both accurate and accountable.
Agencies, in particular, will need to invest in their technical infrastructure and human resources to meet this challenge. AI is not just an out-of-the-box solution. It needs to be undergirded by a zero-trust framework to protect sensitive information, have a standardized ontology to enable cooperation, and be able to constantly evolve (with sufficient guardrails to maintain explainability).
By prioritizing explainable AI in its procurement, development, and deployment, one can use this tool to take advantage of this tool that can improve operational effectiveness without trading off public confidence. This requires significant investment in both technical infrastructure and human capital. The government cannot get this wrong.
