Explainability In Computer Vision

Computer vision models, particularly deep neural networks (DNNs) and convolutional neural networks (CNNs), are driving innovation across industries like healthcare, security, autonomous vehicles, and finance. They offer unprecedented accuracy and automation, but there’s a catch—they often operate as black boxes. When businesses can’t explain their AI’s decisions, they risk compliance failures, legal liability, and consumer distrust. A misdiagnosed disease, a biased facial recognition system, or a flawed autonomous driving decision isn’t just a technical issue—it’s a business risk.

With regulatory frameworks like the EU AI Act demanding transparency—especially in high-risk applications—companies can no longer afford to treat explainability as an afterthought. Ensuring AI models make decisions based on clear, understandable factors isn’t just about compliance; it’s about building trust, mitigating risks, and staying competitive.

Understanding and implementing explainability in computer vision is crucial to future-proofing your AI strategy.

See how computer vision is transforming industries and keeping businesses ahead—learn more now!

This article cuts through the complexity and highlights practical, high-impact techniques for enforcing explainability in computer vision models. It won’t cover every method, but it will demonstrate how the right strategies can keep your AI both powerful and compliant. Here’s what we’ll explore:

• Models that are explainable by nature

• Why deep learning models need post-hoc explainability

• Interpreting individual predictions (local explainability)

• How overall model behaviour can be understood (global explainability)

• The most popular algorithms and techniques for their respective use-cases

• Some real-world examples of how these techniques are used

Explainable Models

One of the most effective ways to ensure explainability in computer vision is to use models and techniques that offer clear, human-understandable decision-making processes. Some traditional machine learning models provide intrinsically interpretable predictions, allowing users to trace how and why a particular classification or detection was made. When applicable, using these approaches instead of black-box deep learning models enhances trust, regulatory compliance, and debugging efficiency. While deep learning remains the dominant choice for high-performance vision tasks, simpler, transparent alternatives can sometimes provide sufficient accuracy while maintaining interpretability.

Decision Trees & Rule-Based Approaches

• In structured image analysis, decision trees provide clear if-then logic based on extracted image features such as colour histograms, texture metrics, or object dimensions.

• Example: In industrial defect detection, a decision tree can classify products based on features like edge sharpness, shape irregularity, or colour deviations, allowing human inspectors to verify decisions easily.

Generalised Additive Models (GAMs) for Vision Features

• GAMs allow each feature to have a separate, interpretable contribution to the final decision, making them valuable when working with engineered vision features instead of raw images.

• Example: In retinal disease classification, a GAM can model how blood vessel thickness and lesion area contribute to a diagnosis, offering insights that align with medical knowledge.

Prototype-based Learning (ProtoPNet, TesNet)

• Rather than relying on abstract high-dimensional features, Prototype Propagation Networks (ProtoPNets) classify images based on learned visual prototypes—reference patches that resemble parts of the input image.

• Example: In wildlife classification, a ProtoPNet model can explain a decision by showing which feather pattern or beak shape most influenced the classification of a bird species.

Visual explanations produced by GAM for similarity and classification tasks. Source: Hertz, 2021

Post-hoc Interpretability

While inherently interpretable models are ideal, high-performance computer vision applications often rely on deep learning architectures like convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid architectures. These models process vast amounts of visual information, making them effective but difficult to interpret. To bridge this gap, post-hoc interpretability methods provide insights into how a model arrives at specific predictions.

These methods fall into two categories:

• Local Explainability (Instance-Level) – Understanding why a model made a particular decision for a single input.

• Global Explainability (Model-Level) – Understanding the general behaviour of a model across all inputs.

Balancing performance with interpretability is becoming increasingly important, especially as regulations push for greater transparency in AI.

Learn more about how explainability techniques can make computer vision both powerful and accountable!

Local Explainability (Instance-Level):

Local interpretability techniques help us examine individual predictions, which is especially useful for debugging misclassifications, ensuring fairness, and providing explanations in critical applications like medical imaging and autonomous driving. The most popular methods are as follows:

LIME (Local Interpretable Model-Agnostic Explanations)

LIME helps explain how small changes in an image affect a model’s decision by:

• Modifying image pixels or segments and observing changes in the model’s predictions.

• Training a simpler model (such as logistic regression or decision tree) on these perturbed samples to approximate how the deep learning model behaves locally.

• Producing feature importance maps that highlight which areas of the image contributed most to the classification.

An image example explaining the process behind LIME. Source: Ras, Xie, van Gerven and Doran, 2020

Example: In medical imaging, if a model classifies an X-ray as pneumonia-positive, LIME can highlight the specific lung regions that influenced the decision. If the explanation focuses on irrelevant areas (e.g., the edge of the scan), it signals a problem with the model.

Example: In autonomous driving, LIME can help determine whether a self-driving car detected a pedestrian based on their full body or just a small part (like a shadow or a reflection), ensuring that the model learns meaningful features.

SHAP (Shapley Additive Explanations)

SHAP is a more mathematically rigorous approach based on cooperative game theory, assigning an importance value to each feature by:

• Evaluating all possible combinations of image features (e.g., pixel groups, object parts) to quantify their contribution to a model’s decision.

• Providing a global and local understanding of which areas in an image most influenced a prediction.

A visualisation of the SHAP algorithm. Source: Ras, Xie, van Gerven and Doran, 2020

Example: In facial recognition, SHAP can explain whether a model identified a person based on their eyes, nose, or jawline. If the model is overly sensitive to hairstyle instead of facial structure, it may indicate bias.

Example: In satellite imagery classification, SHAP can reveal whether an AI system detects deforestation based on actual tree loss patterns or irrelevant artefacts like cloud cover.

While SHAP is more computationally expensive than LIME, it offers more consistent and reliable feature attribution, making it preferable for high-risk applications.

Global Explainability (Model-Level):

While local explanations help interpret single predictions, global explainability methods provide insights into how a model makes decisions across all images, helping researchers improve transparency, debug systematic biases, and ensure compliance.

Feature Importance Scores

Feature importance scores measure how much a specific feature (or pixel, region, or visual attribute) contributes to the overall predictions of a model. These scores rank features based on their influence on model performance.

Permutation Importance:

• Measures how much model accuracy drops when specific features (e.g., colour, texture, shape, view angle) are randomly shuffled.

• A steep drop in accuracy indicates that a feature is highly important.

CNN Feature Maps & Attention Scores:

Vision models often use feature maps in CNNs or attention scores in vision transformers to weigh different parts of an image.

These values help researchers understand which visual features (edges, textures, object parts) contribute most to a model’s classification.

A label overlay of a training image. Source: Gruosso, Capece and Erra, 2020

Example: In security camera footage analysis, a feature importance ranking might reveal that an AI model relies too heavily on background lighting rather than facial features when identifying individuals, leading to biased results in poorly lit environments.

Partial Dependence Plots (PDPs) & Accumulated Local Effects (ALE)

Both PDPs and ALE show how a model’s predictions change when varying a single feature, while keeping others constant.

PDPs:

• Show how a specific visual feature (e.g., object size, texture contrast, brightness) affects the model’s output.

• Useful for detecting non-linear relationships, like how a slight blur might have little effect, but excessive blur makes objects unrecognisable.

• In CNN-based object classification, PDPs can show how brightness, contrast, or texture patterns affect predictions.

• In ViTs, PDPs can be applied to patch embeddings to analyse how altering a specific region of an image affects classification confidence.

• In traditional image-based machine learning (e.g., SVM or Random Forest on feature descriptors), PDPs show how modifying a single visual property (e.g., edge density, colour histogram) impacts predictions.

ALE:

• A more advanced alternative to PDPs that corrects for feature correlations, making it more reliable for high-dimensional visual data.

Example: In medical imaging, PDPs could analyse how different tumour sizes affect cancer predictions, ensuring that a model correctly weighs small and large tumour growths.

Example: In drone surveillance, ALE can help determine whether an AI model correctly factors in shadow changes when identifying moving objects.

Surrogate Models (Simplified Model Approximation)

Surrogate models approximate the behaviour of a black-box AI system using an inherently interpretable model (such as a decision tree or rule-based classifier).

Process:

• Train a deep learning model for a complex task.

• Use its outputs to train a smaller, explainable model.

• Analyse the surrogate model to gain insight into decision rules and feature contributions.

Example: In diabetic retinopathy detection, a deep learning model trained on retinal images can be approximated by a decision tree, revealing that it prioritises blood vessel thickness and lesion size when diagnosing the disease.

Example: In autonomous vehicle object detection, a surrogate model can approximate how a deep network detects road signs, showing whether it focuses on sign shape, text, or background context.

Conclusion

As AI adoption grows, so does the need for models that are not only accurate but also accountable and transparent. Businesses that invest in explainability gain a competitive edge—they can debug faster, mitigate risks, and build AI systems that regulators and users can trust.

However, implementing explainability effectively requires the right expertise and tools. At Technolynx, we specialise in designing and optimising computer vision systems that balance performance, interpretability, and compliance. Whether you’re refining an existing model or building AI into your product, we can help you develop a solution that’s both powerful and explainable.

See how explainability can strengthen your AI strategy— get started here!

References:

• Hertz, A. (2021) GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps. arXiv preprint arXiv:2109.00951.

• Freepik. (n.d.) Chatbot technical support artificial intelligence software flat composition with robot answering customer questions illustration. MacroVector

• Gruosso, M., Capece, N. and Erra, U. (2020) Human segmentation in surveillance video with deep learning. Multimedia Tools and Applications, 80, pp. 1175-1199. doi:10.1007/s11042-020-09425-0.

• Ras, G., Xie, N., van Gerven, M. and Doran, D. (2020) Explainable Deep Learning: A Field Guide for the Uninitiated. arXiv preprint arXiv:2004.14545.