Generative AI vs Explainable AI

As public sector leaders navigate regulatory pressure, accountability requirements and growing public scrutiny, one distinction is becoming increasingly important: the difference between Generative AI vs Explainable AI (XAI).

Understanding this difference is critical not only for successful AI adoption, but for ensuring ethical, transparent and controllable use of data across government services.

What Is Generative AI?

Generative AI refers to systems that create new content based on patterns learned from large datasets. This can include:

• Text generation (e.g. reports, emails, summaries)
• Image and video creation
• Code generation
• Synthetic data production

Popular large language models fall into this category. They are powerful, fast and increasingly accessible – but they come with inherent challenges for public sector use.

Key characteristics of Generative AI:

• Operates as a black box – outputs are difficult to trace back to specific inputs or decision paths
• Often trained on vast, mixed-quality datasets, raising data provenance concerns
• Can produce hallucinations – confident but incorrect outputs
• Limited auditability and justification for decisions

While Generative AI can deliver productivity gains, its lack of transparency can conflict with public sector obligations around fairness, explainability, compliance and trust.

What Is Explainable AI (XAI)?

Explainable AI, by contrast, is designed to ensure that AI decisions can be understood, interpreted and justified by humans.

Rather than focusing on content creation, Explainable AI prioritises:

• Decision transparency
• Clear reasoning paths
• Governance and control
• Accountability at every stage

This makes XAI particularly well‑suited to environments where decisions must be defensible – such as government, defence, healthcare, policing and regulatory bodies.

Key characteristics of Explainable AI:

• Clear visibility into how outcomes are reached
• Easier auditing and compliance with regulations
• Reduced risk of unintended bias or data misuse
• Greater confidence for operational and policy decisions

In short, Explainable AI supports better decisions, not just faster outputs.

Why Explainable AI Is Better Aligned to Public Sector Needs

Public sector organisations operate under a unique set of constraints and responsibilities. AI systems must align with legal, ethical and societal expectations, not just technical performance.

1. Accountability and Auditability

Government bodies must be able to explain decisions to citizens, regulators and auditors. Explainable AI enables:

• Transparent decision trails
• Evidenced outcomes
• Clear accountability for automated processes

This is essential where AI influences benefits allocation, risk assessments or policy recommendations.

2. Stronger Data Governance

Explainable AI relies on well‑structured, classified and trusted data. This ensures:

• Sensitive data is used appropriately
• Records management policies are enforced
• AI outputs remain grounded in authoritative sources

Without this foundation, AI systems can quickly become unreliable and non‑compliant.

3. Reduced Operational and Reputational Risk

Black‑box AI systems introduce risks around bias, data leakage and misinformation. Explainable AI:

• Makes risks visible and manageable
• Supports proactive control rather than reactive correction
• Aligns with emerging AI governance frameworks and UK public sector guidance

This is particularly important in secure or high‑impact environments.

4. Greater Public Trust

Trust is critical to digital transformation. Citizens expect government to use data responsibly and fairly. Explainable AI helps build confidence by ensuring decisions are transparent, justifiable and human‑understandable.

The Missing Ingredient: Trusted Data and Records Foundations

Even the most carefully designed AI model will fail without the right data foundations in place.

Public sector organisations often face challenges such as:

• Legacy records systems
• Poor data classification
• Inconsistent metadata
• Siloed or unstructured information

These issues make it difficult to deploy Explainable AI at scale.

How Certes IT Enables Explainable AI Through Data & Records Transformation

Certes IT’s Data & Records Transformation Service is designed to address these challenges directly, creating the conditions needed for safe, explainable and effective AI adoption.

Our service helps public sector organisations to:

• Transform and structure data so it is usable, traceable and trustworthy
• Classify and govern records in line with regulatory and security requirements
• Establish clear data lineage, supporting explainability and auditability
• Enable AI‑ready environments where decision‑making can be controlled and justified

By strengthening data governance and records management, organisations gain the confidence to deploy Explainable AI in a way that is compliant, ethical and operationally robust.

Learn more about our Data & Records Transformation Service here

Generative AI and Explainable AI: Not Either/Or

It’s important to note that this is not an argument to abandon Generative AI altogether. In many cases, the most effective approach is a hybrid model:

• Generative AI for productivity and insight generation
• Explainable AI for decisions, recommendations and outcomes

However, in public sector environments, Explainable AI must always lead when decisions affect people, policy or critical services.

Final Thoughts on Generative AI vs Explainable AI

As AI continues to evolve, public sector organisations must look beyond what AI can do and focus on what it should do.

Explainable AI offers:

• Greater control
• Stronger governance
• Reduced risk
• Increased public trust

When built on trusted, transformed data foundations, it becomes a powerful enabler for responsible innovation.

With the right strategy and the right partner, public sector organisations can harness AI not as a black box, but as a transparent, accountable and defensible decision‑making tool.