Unveiling the Secrets: Understanding White Box AI

Technological innovations have expanded the parameters of what we thought artificial intelligence (AI) could do. As AI gets introduced to more contexts with direct and important impacts on humans -- and as decisions come to be more obviously and visibly life or death or life altering -- the desirability of explainable, accountable and understandable AI is growing. Transparent, explainable machine learning algorithms have demonstrated benefits and use cases. The notion of white box AI indicates AI systems with algorithmic transparency and human-comprehensibility. While glass box AI and transparent AI may be more accurate and understandable monikers, the concept is what's most important. This article delves into the concept of white box AI, contrasting it with its counterpart, black box AI, and exploring its significance across various sectors.

The Essence of White Box AI

White box AI, sometimes called glass box, is transparent about how it comes to conclusions. Humans can look at an algorithm and understand its behavior and the factors influencing its decision-making. Therefore, white box algorithms give a result and clearly readable rules. In simple terms, white box models are like an open book - you can see exactly how they make decisions. You can trace every step of their decision-making process, and nothing is hidden from you. That’s what makes them so transparent and easy to interpret.

Two key elements make a model white box: features must be understandable, and the ML process must be transparent.

Contrasting with Black Box AI

More opaque ML algorithms (also called the black box approach) can produce useful results that organizations and even governments embrace. Yet they don't ensure that humans understand why they produced these outputs or decisions. Black box AI is any AI system whose inputs and operations aren't visible to the user or another interested party. In simple terms, black box is an impenetrable system.

Because they are built on neural networks or other opaque architectures, the inner workings of these systems -- the processes by which an output is generated based on an input -- are not explainable. Deep neural networks (DNNs) and deep learning algorithms create thousands of non-linear relationships between inputs and outputs. The complexity of the relationships makes it difficult for a human to explain which features or interactions led to a specific production. Black box AI models arrive at conclusions or decisions without explaining how they were reached. Therefore, it becomes increasingly challenging to identify why an AI model produces biased outputs and where errors in logic are occurring. Invisibility also makes it difficult to determine who should be held accountable when outputs are flawed or dangerous. Humans have failed to understand black box AI's internal mechanisms and contributing factors.

The Importance of Explainability

As AI increases, there is a growing concern about the lack of transparency in its decision-making process. AI's “black box” nature can leave us uncertain and skeptical, wondering how AI arrives at its recommendations and decisions. Explainable AI, created so that a typical person can understand its logic and decision-making process, is the antithesis of black-box AI. They can explain to those affected by a decision how that decision was made, thus making explainable AI and accountable AI two new buzzwords in the ever-growing AI lexicon. They can verify the AI is not making decisions based on incorrect data. They can ensure the AI is not ignoring relevant information available to it. They can find accidental and undesirable biases in algorithms. They can identify maliciously introduced biases in algorithms.

IBM defines explainable artificial intelligence (XAI) as a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI describes an AI model, its expected impact, and potential biases. XAI helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision-making. With XAI, humans can gain insights into the rationale behind AI recommendations and decisions, making them more trustworthy and understandable.

Why Explainability Matters for AI/ML Practitioners

In machine learning, interpretability (how well you can explain a model’s behavior) and explainability (how easy it is to understand what a model does) are key factors that influence how models are used in the real world. If you’ve ever tried explaining a deep learning model to a non-technical stakeholder, you’ll know exactly what I’m talking about. It’s not always easy to break down something that operates like a black box.

So, whether you’re developing a model for predicting stock prices or building a recommendation system, being able to explain your algorithm’s decisions will not only make your life easier but also help your clients and stakeholders trust your work more.

Examples of White Box Algorithms

You’ve likely worked with some white box algorithms already, even if you didn’t know it at the time. Here are some familiar ones:

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Decision Trees: Imagine a flowchart where each decision (like “Is the person over 50?”) leads to another until a conclusion is reached. Simple, right? Decision tree algorithms, for example, follow a logical ‘if this, then that’ pattern. For instance, if a patient has a cough, a high temperature, a sore throat, and has lost their sense of smell, the algorithm may ‘diagnose’ the patient as having COVID-19.
Linear Regression: This model tries to draw a straight line through your data points to predict outcomes, like predicting housing prices based on square footage.
Logistic Regression: Don’t let the name fool you; this one isn’t just about regression. There’s no hidden logic or complex computations that you can’t explain. Logistic regression was developed in the early 1800s, and re-popularized in the 1900s. It’s been around for a long time, for many reasons. It solves a common problem (predict the probability of an event), and it’s interpretable. Here is the logistic equation defining the model:
There are three types of variables in this model equation:
- p is the probability of an event we’re predicting. For example, defaulting on a loan
- The x's are features. For example, loan amount.
- The 𝛽's (betas) are the coefficients, which we fit using a computer.
The betas are fit once to the entire dataset. The x’s are different for each instance in the dataset. The p represents an aggregate of dataset behavior: any dataset instance either happened (1) or didn’t (0), but in aggregate, we’d like the right-hand side and the left-hand side to be as close as possible.

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The “log(p/(1-p))” is the log odds, also called the “logit of the probability”. The odds are (probability the event happened)/(probability the event won’t happen), or p/(1-p). Then we apply the natural logarithm to translate p, which takes the range 0 to 1, to a quantity which can range from -∞ to +∞, suitable for a linear model.
This model is linear, but for the log odds. That is, the right-hand side is a linear equation, but it is fit to the log odds, not the probability of an event.
This model is interpretable as follows: a unit increase in xi is a log-odds increase in 𝛽i.
For example, suppose we’re predicting probability of loan default, and our model has a feature coefficient 𝛽1=0.15 for the loan amount feature x1. That means a unit increase in the feature corresponds to a log odds increase of 0.15 in default. We can take the natural exponent to get the odds ratio, exp(0.15)=1.1618. That means:
for this model, a unit increase (of say, a thousand dollars) in loan amount corresponds to a 16% increase in the odds of loan default, holding all other factors constant.
This statement is what people mean when they say logistic regression is interpretable.
To summarize why logistic regression is a white box model:
- The input response terms (𝛽ixi terms) can be interpreted independently of each other
- The terms are in interpretable units: the coefficients (betas) are in units of log odds.
Generalized Additive Models (GAMs): Generalized Additive Models (GAMs) were developed in the 1990s by Hastie and Tibshirani. Here is the equation defining the model:
This equation is quite similar to logistic regression. It has the same three types of elements:
- E(Y) is an aggregate of dataset behavior, like the “p” in the equation above. In fact, it may well be the probability of an event, the same p.
- g(.) is a link function, like the logit (or log odds) from the logistic equation above.
- fi(xi) is a term for each dataset instance feature x1,…,xm.
The big difference is instead of a linear term 𝛽ixi for a feature, now we have a function fi(xi). In their book, Hastie and Tibshirani specify a “smooth” function like a cubic spline. Lou et al. looked at other functions for the fi, which they call “shape functions.”
A GAM also has white box features:
- The input response terms (f(xi) terms) can be interpreted independently of each other
- The terms are in interpretable units. For the logit link function, these are log odds.
Now a term, instead of being a constant (beta), is a function, so instead of reporting the log odds as a number, we visualize it with a graph. In fact, the graph above of pneumonia risk of death by age is one term (shape function) in a GAM.
GAMs with interaction terms (GA2Ms): GA2Ms were investigated in 2013 by Lou et al. Here is the model equation:
This equation is quite similar to the GAM equation from the previous section, except it adds functions that can account for two feature variables at once, i.e. interaction terms.
Microsoft just released a library InterpretML that implements GA2Ms in python. In that library, they call them “Explainable Boosting Machines.”

Characteristics of White Box Algorithms

Interpretability: If someone asks, “Why did the model make this decision?” you can show them exactly how it was done. There’s no guessing or assumptions involved.
Auditability: Need to go back and review how a decision was reached? With white box models, it’s easy to audit their steps and trace back the logic behind every decision.

Examples of Black Box Algorithms

You might already be using black box algorithms in your machine learning projects without even realizing it. Some of the most common examples include:

Deep Learning (Neural Networks): These are the kings of black box algorithms. Neural networks have so many layers of abstraction that it’s nearly impossible to explain why a specific decision was made.
Random Forests: While individual decision trees are more interpretable (and thus “white box”), a random forest ensemble with hundreds of trees can behave like a black box, especially when trying to explain why a particular path was taken.
Support Vector Machines (SVMs): SVMs can be powerful for classification tasks, but understanding the exact decision boundary they draw is often not straightforward.
Gradient Boosting Machines (GBMs): Like random forests, GBMs combine multiple models to improve performance, but at the cost of interpretability.

Characteristics of Black Box Algorithms

Complexity: These models are built from layers upon layers of computations. For example, deep learning models, with their hundreds (or even thousands) of neurons, work in ways that are difficult to trace. It’s like trying to explain how every single neuron in a human brain works together to form a thought - possible in theory, but impractical in practice.
Opacity: The primary appeal of black box algorithms - performance - comes at a cost: transparency. The decision-making process is hidden, making it impossible to fully understand how or why certain decisions are made.
Performance Tradeoff: Black box algorithms often outperform white box models when it comes to prediction accuracy, especially on complex tasks like image recognition or natural language processing. But you pay for that extra performance with a loss in interpretability. If you need a crystal-clear explanation of how your model works, black box algorithms will leave you scratching your head.

Use Cases for White Box AI

So, where do white box models shine the most? Here’s a hint: Think of industries where transparency isn’t just a nice-to-have - it’s a legal requirement.

Finance: In banking and credit scoring, you need to explain why a customer was denied a loan. White box models can help ensure that decision-making is transparent and complies with regulations.
Healthcare: When making life-and-death decisions, like predicting the risk of a heart attack, it’s crucial that doctors can understand and trust the model’s predictions. If an AI algorithm being used as part of the decision-making process, for example to help in diagnosing patients, it is still important that the doctor can explain the ‘decision’ (e.g., the classification or prediction) made by that algorithm.
Legal: In law, decisions need to be fully auditable, so everyone can see how they were made, especially when biases need to be minimized.

And let’s not forget about GDPR and other data protection regulations. These laws often require that models be explainable, especially when they’re used to make decisions about people. That’s a major reason why white box models can be a safe choice in certain industries.

Use Cases for Black Box AI

So when do you actually want to use black box algorithms? Here are a few scenarios where they shine:

Recommender Systems: Think about the algorithms behind Netflix’s movie recommendations or Amazon’s product suggestions. They rely on tons of data and highly complex models to make predictions. The exact logic of why a certain movie was recommended might not be clear, but the accuracy of the recommendation is what matters most.
Fraud Detection: In the world of banking and e-commerce, black box models like GBMs are used to detect fraudulent activity. While it may be hard to explain every decision, these models are trusted because they’ve been shown to catch fraud better than simpler, more transparent models.
Autonomous Systems: When you think of self-driving cars, black box algorithms are likely playing a huge role behind the scenes. These models process large amounts of sensor data in real-time, making life-or-death decisions. Accuracy is paramount, and while explainability is important, it often takes a backseat to performance in these scenarios.

White Box vs. Black Box: Interpretability vs. Performance

Now comes the million-dollar question: Should you go for interpretability or performance? Let me put it this way - you’re standing at a crossroads, and you need to make a choice. On one side, you’ve got white box models that offer crystal-clear interpretability, but sometimes lack the horsepower to handle complex tasks. On the other side, there’s black box models that can deliver jaw-dropping performance, but good luck explaining how they arrived at their decisions.

For instance, if you’re working on a healthcare application that predicts whether a patient might develop a particular illness, a white box model might be your best bet. Why? Because transparency is critical in healthcare - you need to know exactly why the model made that decision. But if you’re building a recommendation system for an e-commerce site, where accuracy and personalization matter more than explainability, a black box algorithm might be the way to go.

When to Choose Which?

You might be wondering: “When do I pick one over the other?” Well, here’s a quick guide to help you decide:

Small Data vs. Big Data: If you’re working with a small dataset where interpretability is critical - like a medical study or legal case - white box models are often a better fit. They’re simple, easy to interpret, and less prone to overfitting with smaller datasets. But if you’ve got a huge dataset with complex patterns, say from social media or e-commerce activity, black box models will likely give you better predictive power.
Regulation and Compliance: Industries that are heavily regulated, like finance and healthcare, often require white box models. In these fields, it’s crucial to be able to explain decisions and ensure compliance with regulations like GDPR or HIPAA. Meanwhile, performance-centric industries - like advertising or image recognition - where the focus is more on precision than explainability, are prime candidates for black box algorithms.

Model Explainability Techniques

But what if you want the best of both worlds? This might surprise you, but there are ways to make black box models more interpretable. Techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) are used to explain the predictions of black box models. While these tools won’t make the model itself more transparent, they can give you a better understanding of how specific decisions were made.

Throughout the years, researchers have developed tools to prevent black box AI and ensure responsible AI, including:

LIME (Local Interpretable Model-Agnostic Explanations)
SHAP (SHAPley Additive exPlanations)
ELI5 (Explain Like I’m 5)
DALEX (Descriptive mAchine Learning EXplanations)

Hybrid Approaches: Combining the Best of Both Worlds

Imagine trying to blend the best traits of two completely different people - one who’s great at math but terrible at communication, and another who’s an excellent speaker but not so strong with numbers. What if we could merge them into one? That’s the essence of hybrid approaches in machine learning. They aim to combine the interpretability of white box models with the performance of black box algorithms. It’s like having your cake and eating it too.

Model Simplification

This might surprise you, but even complex black box algorithms can be simplified to give us some insight into how they work. One approach is to use model simplification techniques. For example, after training a complex model like a neural network or random forest, you can create a simpler, more interpretable model - like a decision tree surrogate - to approximate the behavior of the original model.

Here’s the deal: the decision tree won’t be as accurate as the black box model, but it can provide an intuitive way to understand what’s happening under the hood. You’re essentially creating a “shadow” model that mirrors the behavior of the complex model, but in a way that humans can easily interpret.

Explainable AI (XAI)

You might be wondering: “Is there a bigger movement to tackle this explainability problem?” Absolutely, and it’s called Explainable AI (XAI).

The XAI movement is all about making even the most complex models more interpretable without sacrificing too much performance. Instead of simplifying models after the fact, XAI techniques aim to explain the decisions of black box algorithms in a way that humans can understand.

Two of the most popular tools in this space are LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). These tools don’t modify the model itself. Instead, they help you interpret the predictions by breaking down the contribution of each feature to the final decision.

The Future of White Box AI

Any AI use case can theoretically be a white box AI use case. There is no reason an AI system must be opaque or unexplainable. However, interest in AI that is transparent and explainable is highest in environments closely linked to human wellbeing. controlling medical devices. In these and many other situations, employees in government, banking, medicine, law enforcement and justice organizations want to answer questions about how and why decisions were made and defend them as reasonable decisions. Because there are fewer commercially available white box AI systems to use in building a solution, organizations requiring one must build their own for the foreseeable future. However, many academic programs that teach AI, including programs at Johns Hopkins and Michigan State, include courses covering the ethics of AI and courses teaching white box techniques.

The scope of white box AI's role in the future depends on two factors. First, it depends on the accumulation of expertise and evolution of underlying platforms. As we have seen in the black box space recently, once there are enough skilled people, mature projects and focused startups in a space, progress accelerates sharply. The implications of existing legal frameworks, like the GDPR, for how AI can and must function are still being worked out. New laws and agency rules will follow in jurisdictions around the world.

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