Explainable AI – Making Your Gen AI Understandable

As Generative AI (Gen AI) becomes more embedded in business processes, the demand for transparency and interpretability increases. Particularly in sectors like finance, investment, healthcare, and law—where decisions carry significant weight—understanding how Gen AI models reach their conclusions is not optional. It is essential.

In this post we will explore the importance of explainability in Gen AI, what it means in practice, why it matters for compliance, trust, and accountability, and how businesses can approach the challenge of making their Gen AI systems more transparent and understandable.

Why Explainability Matters

Explainability refers to the ability to describe how and why a Gen AI system produced a particular output. For traditional AI, this might involve identifying which input variables most influenced an outcome. For Gen AI, which generates content - be it text, code, or visuals - the need for clear explanations becomes even more crucial.

The reasons are threefold. First, explainability builds trust. When stakeholders can understand how an AI model works, they are more likely to use and rely on it. Second, explainability is necessary for compliance. Regulatory environments, especially in financial services, demand justification for automated decisions. Third, explainability helps improve the model. Understanding its reasoning can highlight biases, gaps, or areas for improvement.

Without explainability, Gen AI outputs can feel like a black box - seemingly correct, but with no clear understanding of why. This undermines confidence and increases risk.

Explainability in Sensitive Industries

Some industries face more scrutiny than others when it comes to AI transparency. In finance, for instance, firms must comply with regulations that require them to explain the rationale behind lending decisions, fraud alerts, or investment recommendations. A fund manager cannot justify an AI-generated investment strategy to a client - or regulator - without understanding how it was produced.

Healthcare is another high-stakes environment. If a Gen AI system suggests a course of treatment based on clinical notes or scans, doctors must understand the basis for the recommendation. Without this, they risk making inappropriate decisions or facing legal exposure.

The same applies to legal services, insurance underwriting, public services, and HR. In each case, explainability is necessary to ensure decisions are defensible, ethical, and legally sound.

What Explainability Looks Like in Gen AI

For Gen AI, explainability does not mean revealing every mathematical layer of a large language model. Most stakeholders don’t need or want that level of detail. Instead, they want accessible explanations about the rationale, inputs, and reliability of the AI’s output.

A well-explained Gen AI system should answer questions like:

• What data was used to generate this result?

• What assumptions were made?

• How certain is the model about this outcome?

• Are there limitations or known blind spots in this kind of task?

For example, if a Gen AI system summarises a legal document or recommends a financial product, it should include a short explanation of what data was considered and the key factors driving the suggestion. Confidence indicators - such as a percentage estimate or natural language cue (e.g. "based on similar cases") - can also improve understanding.

Gen AI’s Unique Challenges with Explainability

While traditional machine learning systems often involve structured data and relatively interpretable models, Gen AI introduces new challenges. It generates open-ended outputs using massive neural networks trained on diverse data. This makes it harder to track exactly how and why a specific piece of content was produced.

Large language models (LLMs), for example, work by predicting the next word in a sequence based on probabilities learned from training data. Their outputs are coherent and often accurate - but explaining why a specific phrase or idea was included can be difficult.

Moreover, Gen AI systems sometimes “hallucinate” - producing content that sounds plausible but is factually incorrect. Without explainability mechanisms, users may not realise the flaws in the output.

Techniques for Improving Explainability

One often overlooked method of improving explainability lies in how prompts are crafted. Practical prompting techniques can encourage Gen AI systems to reveal their reasoning more clearly, especially in tasks that involve decision-making or step-by-step analysis.

A useful approach is the Chain-of-Thought Prompting method. This involves prompting the model to break down its reasoning into a series of logical steps, rather than jumping straight to an answer or conclusion. For example, instead of asking "What’s the risk profile of this investment?", a chain-of-thought prompt might be: "Analyse the key indicators that influence this investment’s risk profile and explain how each contributes to your final assessment." This encourages the model to output a more transparent and structured response.

Similarly, Few-Shot Prompting can improve explainability by providing examples of well-reasoned outputs before requesting a new one. This primes the model to mimic the style of explanation shown in the examples.

These prompting strategies can be particularly helpful in compliance-heavy fields, where detailed justification is essential, and in customer-facing applications, where clarity enhances trust.

Businesses can adopt several practical strategies to improve explainability in their Gen AI systems:

1. Prompt Design Transparency: Clearly display or log the prompt used to generate each output. This allows users to assess whether the input was appropriate and relevant.

2. Source Referencing: Where possible, link AI-generated content to specific source documents or datasets. This is especially helpful in research, legal, or financial contexts.

3. Confidence Scores and Summaries: Include probability scores or human-readable explanations of how confident the model is in its output. Some platforms now provide natural language rationales alongside results.

4. User Feedback Loops: Allow users to rate the clarity and relevance of outputs, feeding this information back into model refinement.

5. Explainable AI Tools: Use model-agnostic tools such as LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) to help illuminate which factors influenced the model’s output.

6. Human Review and Validation: For high-stakes decisions, ensure AI outputs are reviewed by subject-matter experts who can assess and contextualise results.

Regulatory and Legal Considerations

Explainability is increasingly becoming a legal requirement. Under GDPR’s “right to explanation,” individuals affected by automated decisions can request information about how those decisions were made.

In the UK, the Financial Conduct Authority (FCA) has issued guidance on explainable AI in financial services. Similar requirements are emerging across healthcare, insurance, and employment law.

These regulations mean that businesses must not only produce explainable AI systems, but also document and retain the reasoning process for audits or disputes. Having explainable systems in place is no longer just good practice - it’s a regulatory expectation.

Balancing Accuracy and Interpretability

There’s often a perceived trade-off between performance and explainability. More complex models, such as deep neural networks, are highly accurate but harder to interpret. Simpler models - like decision trees or linear regressions - are more explainable but may underperform on complex tasks.

One way to address this is to use hybrid systems: Gen AI to generate content and a simpler model or rules-based system to validate, score, or explain the results. Another option is to combine Gen AI outputs with curated explanations, where experts interpret and communicate the rationale to end users.

Businesses should also assess which tasks truly require full explainability. Not every AI-generated output needs a detailed justification - only those with material impact on decisions, compliance, or customer trust.

Building a Culture of Explainability

Explainable AI is not just a technical issue - it’s a cultural one. Businesses must embed explainability into their AI development, deployment, and oversight processes.

This includes training teams to prioritise clarity in prompt design, documentation, and output presentation. It also involves creating standards and protocols for explainability at each stage of the AI lifecycle - from data sourcing and model training to output delivery and review.

Leadership plays a key role. When senior executives demand understandable, trustworthy AI, it sets the tone for responsible adoption. Regular reviews of AI systems, transparency policies, and feedback from users help maintain accountability and drive improvements.

Future Developments in Explainable Gen AI

The field of explainability is evolving rapidly. New techniques are emerging to help interpret large language models more effectively, including attention visualisation, context tracing, and fine-tuning methods that embed explainability features directly into model architecture.

We can also expect greater integration of explainability into AI platforms. Cloud providers are adding tools to help developers build more interpretable systems. Some Gen AI platforms now include explainability settings as standard, allowing users to choose the level of transparency required.

As public expectations rise and regulatory pressure increases, explainable Gen AI will become a defining feature of responsible AI deployment.

Conclusion

Making Gen AI understandable is not a technical luxury - it’s a business necessity. In sensitive sectors and high-stakes decisions, the ability to explain what your AI is doing, and why, is critical to building trust, achieving compliance, and ensuring effective decision-making.

Businesses must approach explainability with the same seriousness as accuracy or efficiency. By embedding explainable practices into Gen AI systems, organisations can gain a competitive advantage - earning the trust of customers, regulators, and their own teams.

In a world where AI is increasingly making decisions, explainability is how we ensure those decisions remain human-centred, accountable, and aligned with our values.