Businesses are increasingly relying on data and AI technologies for operations. This has made it essential to understand how AI tools make decisions. The models within AI applications function like black boxes that generate outputs based on inputs without revealing the intermediary processes. This lack of transparency can result in trust issues and impact accountability.
To overcome these drawbacks, you can use explainable AI techniques. These techniques help you understand how AI systems make decisions and perform specific tasks, helping foster trust and transparency.
Here, you will learn about explainable AI in detail, along with its techniques and challenges associated with implementing explainable AI within your organization.
What is Explainable AI?
Explainable AI (XAI) is a set of techniques that makes AI and machine learning algorithms more transparent. This enables you to understand how decisions and outputs are generated.
For example, IBM Watson for Oncology is an AI-powered solution for cancer detection and personalized treatment recommendations. It combines patient data with expert knowledge from Memorial Sloan Kettering Cancer Center to suggest tailored treatment plans. During its recommendations, Watson provides detailed information on drug warnings and toxicities. This offers transparency and evidence for its decisions.
There are several other explainable AI examples in areas such as finance, judiciary, e-commerce, and autonomous transportation. XAI methods also help you debug your AI models and align them with privacy and regulatory regulations. As a result, by using XAI techniques, you can ensure accountable AI usage in your organization.
Benefits of Explainable AI
Traditional AI models are like ‘black boxes,’ providing minimal insight into their decision-making processes. However, the adoption of explainable AI methods can eliminate this problem.
Here are some of the reasons that make explainable AI beneficial:
Improves Accountability
When you use explainable AI-based models, you can create detailed documentation for AI workflows, mentioning the reasons behind important outcomes. This makes employees involved in AI-related operations answerable for any discrepancies, fostering accountability.
Refines the Working of AI Systems
Using explainable AI solutions enables you to track all the steps within the AI-based workflow. This simplifies bug identification and quick resolution in case of system failures. Using these instances, you can continuously refine and enhance the efficiency of your AI models.
Promotes Ethical Usage
Explainable AI-based platforms facilitate the identification of biases in datasets that you use to train AI models. Following this, you can work to fine-tune and improve the quality of datasets for unbiased results. Such practices encourage ethical and responsible AI implementation.
Ensures Regulatory Compliance
Regulations such as the EU’s AI Act and GDPR mandate the adoption of explainable AI techniques. Such provisions help ensure the transparent use of AI and the protection of individuals’ privacy rights. You can also audit your AI systems, during which explainable AI can provide clear insights into how the AI model makes specific decisions.
Fostering Business Growth
Refining the outcomes of AI models using explainable AI helps you to increase your business’s growth. Through continuous fine-tuning of data models, you uncover hidden data insights, which help frame effective business strategies. For example, the explainable AI approach allows you to enhance customer analytics and use its outcomes to prepare personalized marketing campaigns.
Explainable AI Techniques
Understanding the techniques of explainable AI is essential for interpreting and explaining how AI models work. These techniques are broadly categorized into two types: model-agnostic methods and model-specific methods. Let’s discuss these methods in detail:
Model-Agnostic Methods
Model-agnostic methods are those that you can apply to any AI or machine-learning model without knowing its internal structure. These methods help explain model behavior by perturbing or altering input data and observing the changes in the model’s performance.
Here are two important model-agnostic XAI methods:
LIME
LIME, or Local Interpretable Model-Agnostics Explanations, is a method that provides explanations for the predictions of a single data point or instance instead of a complete model. It is suitable for providing localized explanations for complex AI models.
In the LIME method, you first need to create several artificial data points slightly different from the original data point for which you want an explanation. This is known as perturbation. You can use these perturbed data points to develop a surrogate model. It is a simpler, interoperable model designed to approximate the local behavior of the original model. You can compare the outcomes generated by the surrogate model with those of the original model to understand how a particular feature affects the model’s performance.
For example, to obtain an explanation for an image segmentation app, you can deploy the LIME method. In this process, you should first take an image, which will be divided into superpixels (clusters of pixels) to make the image interpretable. The creation of new datasets follows by perturbing these superpixels. The surrogate model can help analyze how each superpixel contributes to the segmentation process.
SHAP
The SHAP (Shapley Additive Explanations) method uses Shapley values for AI explainability. Shapley values are a concept of cooperative game theory that gives information about how different players contribute to achieving a final goal.
In explainable AI, SHAP implementation helps you understand how different features of AI models contribute to generating predictions. For this, you can calculate approximate Shapley values of each model feature by considering various possible feature combinations.
For each combination, you need to calculate the difference between the model’s performance when a specific feature is included or excluded from the combination. Use the formula below:
Where:
|N|: Total number of features in the AI model.
S: Number of combinations that can be formed for N number of features.
|S|: Number of features in combination S, excluding the feature for which the Shapley value is calculated.
This process is repeated for all combinations, and the average contribution of each feature across these combinations is its Shapley value.
For example, consider you have to use the SHAP method for a housing price prediction model. The model uses features such as plot area, number of bedrooms, age of the house, and proximity to school. Suppose that it predicts a price of ₹ 17,00,000 for some house.
First, obtain the average cost of the houses in the dataset on which the AI model is trained. Let’s assume it is Rs. 10,00,000. Then, calculate SHAP values for each feature, which are found to be as follows:
- + Rs.3,00,000 for a larger plot area.
- + Rs. 2,00,000 for more bedrooms.
- – Rs. 50,000 for an older house.
- + Rs. 1,00,000 for proximity to a school.
Final Price = Baseline + ⅀ SHAP Values
Final Price = 10,00,000 + 3,00,000 + 2,00,000 + 1,50,000 − 50,000 + 1,00,000 = 17,00,000
This explains how the plot area, number of bedrooms, and proximity to school features contributed to the model-predicted house price.
Model Specific Methods
You can use model-specific methods only on particular AI models to understand their functionality. Here are two model-specific explainable AI methods:
LRP
Layer-wise relevance propagation (LRP) is a model-specific method that helps you understand the decision-making process in neural networks (NN). The NN consists of artificial neurons that function similarly to biological neurons. These neurons are organized into three layers: input, hidden, and output. The input layer takes data, processes and categorizes it, and passes it on to the hidden layer.
There are several hidden layers in NN. A hidden layer takes data from the input layer or previously hidden layer, analyzes it, and passes the result to the next hidden layer. Lastly, the output layer processes the data and produces the final outcome. The neurons impact each other’s output, and the strength of the connection between different neurons is measured in terms of weights.
In the LRP method, you calculate the relevance value sequentially from the last neuron, starting from the output layer and working back to the input layer. These relevance values indicate the contribution of a particular feature. You can then create a heatmap of all the relevant values. In the heatmap, the areas with higher relevance values represent high contributing features.
For example, you want to obtain an explanation of an AI software-generated MRI report showing a tumor. When you use the LRP method, it involves:
- Generation of a heatmap from the model’s relevance values.
- The heatmap highlights areas with abnormal cell growth, showing high relevance values.
- This provides an interpretable explanation for the model’s decision to diagnose a tumor, enabling comparison with the original medical image.
Grad-CAM
Gradient weighted class activation map (Grad-CAM) is a model-specific method for explaining convolution neural networks (CNN). A CNN consists of a convolution layer, a pooling layer, and a fully connected layer. The convolution layer is the first layer, followed by several pooling layers, and finally, the fully connected layer.
When you give an image input to a CNN model, it categorizes different objects within the image as classes. For example, if the image contains dogs and cats, CNN will categorize them into dog and cat classes.
In any CNN model, the last convolution layer consists of feature maps representing important image features. The Grad CAM method enables computing the gradient of the output classes with respect to the feature maps in the final convolutional layer.
You can then visualize the gradients for different features as a heatmap to understand how various features contribute to the model’s outcomes.
Challenges of Using Explainable AI
The insights obtained from explainable AI techniques are useful for developers as well as non-experts in understanding the functioning of AI applications. However, there are some challenges you may encounter while using explainable AI, including:
Complexity of AI Models
Advanced AI models, especially deep learning models, are complex. They rely on multilayered neural networks, where certain features are interconnected, making it difficult to understand their correlations. Despite the availability of methods such as Layer-wise Relevance Propagation (LRP), interpreting the decision-making process of such models continues to be a challenge.
User Expertise
Artificial intelligence and machine learning domains have a steep learning curve. To understand and effectively use explainable AI tools, you must invest considerable time and resources. If you plan to train your employees, you will have to allocate a dedicated budget to develop a curriculum and hire professionals. This can increase your expenses and impact other critical business tasks.
Biases
If the AI model is trained on biased datasets, there is a high possibility of biases being introduced in the explanations. Sometimes, explanatory methods also insert biases by overemphasizing certain features of a model over others. For instance, in the LIME method, the surrogate model may impart more importance to some features that do not play a significant role in the original model’s functioning. Due to a lack of expertise or inherent prejudices, some users may interpret explanations incorrectly, eroding trust in AI.
Rapid Advancements
AI technologies continue evolving due to the constant development of new models and applications. In contrast, there are limited explainable AI techniques, and they are sometimes insufficient to interpret a model’s performance. Researchers are trying to develop new methods, but the speed of AI development has surpassed their efforts. This has made it difficult to explain several advanced AI models correctly.
Conclusion
Explainable AI (XAI) is essential for developing transparent and accountable AI workflows. By providing insights into how AI models work, XAI enables you to refine these models and make them more effective for advanced operations.
XAI techniques can be model-agnostic methods like LIME and SHAP. The other type is model-specific methods, such as LRP and Grad-CAM.
Some of the benefits of XAI include improved accountability, ethical usage, regulatory compliance, refined AI systems, and business growth. However, the associated challenges of XAI result from the complexity of AI models, the need for user expertise, inherent biases, and rapid advancements.
With this information, you can implement robust AI systems in your organization that comply with all the necessary regulatory frameworks.
FAQs
How explainable AI is used in NLP?
Explainable AI has a crucial role in natural language processing (NLP) applications. It provides the reason behind using specific words or phrases in language translation or generation of any text. While performing sentiment analysis, NLP software can utilize XAI techniques to explain how specific words or phrases in a social media post contributed to a sentiment classification. You can also implement XAI methods in customer service to explain the decision-making process to customers through chatbots.
What is perturbation in AI models?
Perturbation is a technique of manipulating data points on which AI models are trained to evaluate their impact on model outputs. It is used in explainable AI methods such as LIME to analyze how specific features enhance or deteriorate a model’s performance.