
Why Explainability Matters
In today’s world, AI systems make critical decisions that impact everything from healthcare to social services. However, as noted in AI Explainability in Practice by The Alan Turing Institute, the lack of transparency in AI decisions can erode trust and hinder adoption, particularly in the public sector. Explainability helps bridge this gap by providing transparent insights into AI’s reasoning, fostering accountability and confidence in AI systems, especially when outcomes affect individuals directly.

For instance, a denied service application can be frustrating without a clear, understandable reason. But an explanation showing that eligibility factors (like income level or previous usage) led to this decision helps citizens accept outcomes, even if unfavorable. Making complex AI decisions accessible can transform how the public engages with automated systems, building transparency into the foundation of AI.
Types of AI Explanations
AI explanations come in different types depending on who’s asking the question and what they need to know. In general, we can break them down into two core categories: process-based and outcome-based explanations.
1. Process-based Explanations:
Process-based explanations answer how the AI system works. These explanations are about peeling back the layers of the algorithm to show how data is transformed and decisions are made. Think of it as looking under the hood of a car to explain the engine mechanics.
For example, if a public health organization uses an AI model to predict disease outbreaks, a process-based explanation would dive into the model’s features, the way it weighs different factors, and how it arrives at a prediction. This type of explanation is often needed for those responsible for building, maintaining, or auditing AI systems—such as developers, data scientists, and regulators. They want to know things like:
What data was used to train the model?
How were decisions made at each step of the algorithm?
What does the decision tree or neural network look like?
These explanations give a window into the system’s architecture and provide technical transparency. However, they’re usually too complex for non-experts, making them less suited for end users.
2. Outcome-based Explanations:
On the flip side, outcome-based explanations answer why a specific decision was made. This is more relevant for non-technical users, like citizens affected by an AI decision or policymakers who need to justify a system’s actions.
Sticking with the public health example, imagine a person is denied access to a particular health service because the AI predicted a low risk of disease. The outcome-based explanation would focus on explaining why that prediction was made for that individual—what specific data points led to that conclusion? These explanations typically include:
Which factors most influenced the decision (e.g., age, location, medical history)?
Why were some features considered more important than others?
What are the confidence levels or uncertainties in the prediction?
Outcome-based explanations are designed to make decisions understandable at a human level, providing a clear rationale without getting bogged down in the technical details.
3. Local vs. Global Explanations:
Local explanations focus on explaining a single decision for one specific instance. For example, explaining why a particular loan application was rejected based on the applicant’s credit score, income, and history.
Global explanations aim to explain the overall behavior of the model across many instances. This could involve summarizing how the AI system generally makes decisions for all loan applicants and outlining its key decision-making rules.
4. Post-hoc vs. Intrinsic Explanations:
Post-hoc explanations are generated after the model has already made its decision. These are often used with complex black-box models (like deep neural networks) to explain outcomes after the fact. Methods like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) are popular techniques for creating post-hoc explanations.
Intrinsic explanations come from models that are inherently interpretable by nature. Simple models like decision trees, linear regression, or rule-based systems can be understood without needing extra tools. These models are often used when transparency is a priority from the outset.
5. Context Matters for Explanations:
Different explanations work for different audiences. Engineers or AI developers might need a process-based explanation to debug the system or ensure it’s functioning as expected. Meanwhile, an end user or regulator would be more concerned with outcome-based explanations to justify the fairness or accuracy of individual decisions. This means that designing an AI system requires careful thought about which explanation will matter most to the audience at hand.
AI systems can become more transparent, understandable, and ultimately more trusted by choosing the right explanation type for the right audience.
Balancing Transparency and Complexity
When it comes to explaining AI, there’s always a balancing act between transparency and complexity. AI systems, especially the more powerful ones like deep neural networks, often function as "black boxes." While these models excel at making accurate predictions, their decision-making process is notoriously difficult to understand.
The challenge is finding a way to make AI decisions transparent without oversimplifying the model to the point where it loses effectiveness. So, how do we strike the right balance?
The Case for Interpretable Models
One way to enhance transparency is by using inherently interpretable models. These are models like decision trees, linear regression, or rule-based systems, where the logic behind the decision-making process is clear and straightforward. For example, a decision tree is like a flowchart: at each step, it asks a yes/no question until it arrives at a decision. These models are easy to understand, which makes them excellent choices for applications where explainability is a must.
However, interpretable models can come with trade-offs. They often aren’t as powerful as more complex models like neural networks or ensemble models, especially when the data is high-dimensional or non-linear. So, while they’re transparent, they might not always offer the best performance.
Explaining Black-box Models
When you need the power of a complex model but still require explanations, post-hoc explainability techniques can help. These techniques are used after the model has made its decision to give insight into how that decision was made. Tools like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) are two popular post-hoc methods.
LIME works by creating a simpler, local approximation of the model that’s easier to interpret, focusing on individual predictions.
SHAP provides a global view by calculating how much each feature contributes to the model's output, based on cooperative game theory.
Both of these methods allow you to extract useful explanations from complex, black-box models without compromising their predictive power.
Considerations for Real-World AI Applications
In practice, the right balance depends on the context. For example:
In high-stakes scenarios like medical diagnosis or criminal justice, transparency and fairness may be prioritized over sheer accuracy, making interpretable models a better choice.
In business applications, where decisions must be fast and accurate, a complex model with post-hoc explanations might be more appropriate.
Ultimately, the goal is to ensure that the AI system remains explainable enough to be trusted while still powerful enough to deliver results. The balance between transparency and complexity should always be guided by the needs of the stakeholders and the specific risks involved in the AI’s decisions.
Adapting Explanations for Stakeholders
One size does not fit all when it comes to explaining AI decisions. Different stakeholders have varying levels of technical knowledge, interests, and concerns. Tailoring explanations to these audiences is crucial to ensure transparency and trust in AI systems.
Developers and Data Scientists
For those building and maintaining the AI system, the focus is on process-based explanations. Developers need detailed insights into how the model operates—what algorithms are used, how data is processed, and what parameters influence the model’s behavior. Tools like LIME and SHAP can provide technical breakdowns, helping developers debug and improve model performance. These stakeholders are interested in understanding how the system works under the hood.
Policymakers and Auditors
Policymakers and auditors require explanations that balance technical depth with clarity. They need enough understanding to ensure the AI system complies with legal and ethical standards, without being overwhelmed by technical jargon. These stakeholders focus on governance, fairness, and transparency. They are often responsible for determining if the AI system aligns with societal values and regulations, so they need explanations that emphasize accountability and ethics over technical details.
End Users and the General Public
For end users—citizens, employees, or consumers—the need is for outcome-based explanations that are simple, relatable, and focused on the why. If an AI system makes a decision that impacts their lives, they don’t need to understand the model’s inner workings; they need to know why that decision was made and how it affects them. The explanation should avoid technical complexity and instead provide clear, actionable insights. For example, in healthcare, a patient might want to know why the AI recommends a particular treatment option based on their specific symptoms and history, rather than an abstract explanation of how the model was trained.
Internal Teams and Decision-makers
For business leaders or decision-makers within an organization, explanations should be geared toward the business impact of AI decisions. They need a high-level understanding of how the AI system contributes to organizational goals, improves efficiency, or reduces costs. The focus here is on linking AI decisions to performance metrics—how the system helps achieve targets, what risks are involved, and how the AI aligns with the organization’s strategy.
Balancing Depth and Clarity
The key to adapting explanations is balancing the level of detail and clarity. Developers need depth; policymakers need a blend of depth and clarity, while end users and executives benefit most from clear, concise summaries. Designing AI systems with explainability tools that offer multi-layered explanations ensures that the right information is presented to the right people, at the right time.
Conclusion: The Future of AI Explainability
As AI systems continue to evolve and take on roles in crucial areas like healthcare, public services, and finance, explainability will be a fundamental part of their adoption and success. Transparent and accountable AI isn’t just a best practice; it’s a necessity for building trust, promoting fairness, and ensuring compliance with regulatory standards.
The future of AI will likely demand even more sophisticated tools for explainability, balancing high accuracy with high transparency. By embracing explainability now, organizations can stay ahead of evolving standards and ensure that their AI systems remain reliable, ethical, and ultimately beneficial to society. Building explainable AI isn’t just about meeting today’s needs; it’s about future-proofing AI for tomorrow’s world.
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