Definition
Model Explainability
is a concept that a machine learning model and its output can be explained in a
way that makes it easily understandable to a human at an acceptable level. Usually, ‘Explainability’
and ‘interpretability’ are often used interchangeably. They have something in
common, which is to understand the model. Interpretability is the extent to
which an observer can understand the cause of a decision. It is how accurately humans
can anticipate the result of an AI output, while explainability goes beyond
that and looks at how the AI arrived at the result.
For example, your bank account needs BVN to remain active. That is interpretability. Understanding how the biometric database cross-matches fingerprints, validates identity across multiple banks, and prevents fraudulent account openings is known as explainability.
Illustration of model explainability process [1].
Origin
The concept of
model explainability originated from the emergence of complex AI algorithms and
the need to understand their decision-making processes. As time passed, with AI
applications spreading into critical sectors such as healthcare and finance,
the demand for transparent and interpretable AI models increased dramatically.
The development of the explainable AI framework sought to address these issues,
making interpretability a core requirement in the design of AI systems.
With the rising awareness of the potential risks associated with black-box AI models, stakeholders, including regulatory bodies and industry experts, have stressed the importance of model explainability in guaranteeing the responsible use of AI [2].
Context and
Usage
Model
explainability promotes transparency and interpretability, enabling stakeholders
to understand how decisions are made by AI systems. This makes Model
explainability key in fields that use AI. Utilizing model explainability results
to improved accountability, better user trust, and optimized model performance
across various industries. Some of their applications are as follows;
- Autonomous vehicles: Model explainability clears up how AI systems make navigation and decision-making choices, promoting safety.
- Finance: It is used for risk assessment and credit scoring where understanding model predictions is vital for regulatory compliance.
- Healthcare: By making model decisions clear and interpretable, it improves trust in AI-driven diagnostics.
- Legal: Ensuring fairness in automated decision-making systems by providing explanations for outcomes.
- Marketing: Optimizing targeted advertising strategies through insights gained from explainable models [3].
Why it Matters
The importance of machine learning explainability cannot be overemphasized. As machine learning models, especially deep learning models, become more complex, their decision processes often become a “black box”, unclear and incomprehensible. This lack of transparency creates problems in critical applications where knowing how a model reaches a decision is necessary for ethical, legal, and practical reasons. This is where explainability comes in. Explainability builds trust among users, facilitates regulatory approval, and ensures that AI systems operate in a fair, unbiased manner. Furthermore, it plays a pivotal role in the development and deployment phases by enabling developers to debug and improve models more effectively [4].
In Practice
TFX is an example of a real-life case study of model explainability in action. TFX is a machine learning platform from Google. It provides data validation, preprocessing, model training, and model serving tools. TFX also includes TensorFlow Model Analysis (TFMA), which offers model evaluation and explainability capabilities, such as computing feature attributions and evaluating fairness metrics [5].
See Also
Related Model
Training and Evaluation concepts:
- Model Evaluation: Process of assessing how well a model performs on test data and other metrics
- Model Interpretability: Ability to understand and explain how a model makes decisions
- Model Monitoring: Ongoing tracking of model performance and behavior in production environments
- Model Training: Process of teaching an AI model to make predictions by learning from data
- Model Versioning: Practice of tracking and managing different iterations of AI models over time
References
- Sharma, S., K. (2024). Explainable AI (XAI): Model Interpretability, Feature Attribution, and Model Explainability
- Lark Editorial Team. (2023). Model Explainability in AI
- Lyzr Team. (2025). Model Explainability
- Deepchecks. (2025). Model Explainability
- Boluwatife, V., O. (2023). Explainability in AI and Machine Learning Systems: An Overview