Interpretability Vs. Explainability in AI Models

Interpretability is the ability to understand the inner workings of a model, such as the relationship between input features and the model’s output.

Explainability, on the other hand, pertains to the ability to explain the decision-making process of an AI model in terms understandable to the end user. It focuses on why an algorithm made a specific decision and how that decision can be justified.

The underlying issue with these models is that they’re complex and opaque, making it much more difficult for its own creators to even understand how they generate a certain outcome—As we call them, the black boxes.

A model that predicts crime fell under heat when it was discovered that it’s no better at identifying criminals and also showed traces of racist behavior when it would criminalize black people more than whites.

This predicted the lack of individual recidivism risk compared to random volunteers recruited off the net. 

These recurring issues highlight the need for interpretation and explanation of such sneaky models but, how is interpretability different from explainability when configuring such models?

What are Interpretability and Explainability?

Interpretability and explainability are two very interchangeable concepts when it comes to machine learning models, but they pose different meanings and outcomes and outcomes. 

• Interpretability is the ability of a model to be well understood by humans and relates to the accuracy of machine learning models to associate a cause with an effect.

• While explainability is the extent to which a machine learning system’s mechanics can be explained in human terms— the ability of parameters, deeply hidden in nets, to justify results.

Both concepts help us interpret and understand how and why a model may generate a certain decision, hence the outcome, and particularly more importantly when those decisions affect our lives for example: A model that helps determine whether a person is eligible for a loan or not must be both interpretable and explainable, so that the user or banker who may provide the loan shall understand the criteria and logic behind the decision made to grant justified loan.

Perhaps the lack of transparency can be a genuine concern…

What is the Difference between Interpretability and Explainability?

I’m going to use an example of a predictive AI model that determines whether a given email is spam or not  

Interpretability would be how the model understands and explains the arrival of the prediction to judge the spam emails. Now if the model classifies an email as spam, the interpretation would conclude how the machine noticed a certain pattern in the mail to make a prediction.

Could be the presence of certain keywords, the length of the mail, or the number of exclamation marks used.

Explainability thereon goes beyond the realm of interpretation and its only aim is to make us understand how the model arrives at its predictions but also provides a meaningful and concise briefing on the decision-making process. 

In the case of our spam classification models, the explanation component would help us understand the reasons for classifying the email as spam that is useful to the end user. 

This may be done by highlighting the specific patterns or features that encourage the classification and indicating the outcome.

Interpretability Vs. Explainability?

A linear regression model is interpretable because we can infer how each input variable affects the output variable by eyeing the coefficients.

But let’s talk about deep neural networks like ChatGPT. They’re emulsified in complex hidden layers and nonlinear activations that make it difficult for their own developers to trace the influence of each input on the output. 

If you look at a decision tree model, it’s highly explainable as it lets us follow the branches and nodes and how each decision parameter leads to a certain outcome.

The support vector machine like a facial recognition system is less explainable because it relies on mathematical optimization which may be hard to verbalize or visualize. 

Interpretable AI Models

Interperibtle AI models, also known as white boxes can be understood by humans on their own, without the need for additional tools or techniques to explain their decisions or predictions.

Their internal logic and process is transparent to humans for example: linear models, decision trees, rule-based systems, and some types of neural networks.

The Importance of Interpretability and Explainability

According to a report by FICO and Coronium (2020), 57% responded that the lack of explainability of AI is the barrier to adopting AI in the workplace, while 51% struggled to ensure their AI models are fair and unbiased.

Interpretability and explainability in machine learning are like understanding a chef’s recipe and the reasons behind their cooking choices.

These concepts are super important, especially in serious areas like medicine, just like you’d want to know why a doctor prescribes it, we have to interpret why AI makes certain decisions. This understanding builds trust and helps us check if the AI is fair and not biased.

But, there’s a catch. Sometimes making the AI’s thought process clearer can make it less sharp or precise. So, experts often balance how much they let us peek into an AI’s mind and how well it performs its tasks.

In short, interpretability and explainability help us understand why an AI does what it does, building our trust and making sure it’s fair and good at its job.

How to Measure Interpretability? 

Some possible methods to measure interpretability are:

1. Application-grounded evaluation: This testing happens with real humans in a realistic scenario to measure how well the model can perform a task or make decisions based on its output.

2. Human grounded evaluation: This method involves experiments among humans in the form of surveys, interviews or cognitive tests and measuring to test how nicely the DA understands the output of the model.

3. Functionally grounded evaluation: This method involves defining metrics that may project the model’s outcome for instance simplicity, fidelity, or robustness to calculate how well the model satisfies those criteria.

Pros and Cons of Interpretability 

1. Trust:  Users and stakeholders are likely to have their trust increased when they’re using interpretable models as they can verify and validate the decisions of the model.
   
   A study published in the Journal of the American Medical Association (JAMA) developed an AI model that can predict the patients who were at risk of developing Sepsis. 

The model was based on a small set of easily interpretable features that helped attain high accuracy while still being transparent and interpretable.

2. Debugging: They can help in identifying and correcting any bias errors in the design, data, or algorithm

3. Transferability: Interpretable AI models can enable to generalize and adapt the knowledge of these models to new domains or tasks 

4. Compliance: Some legal or ethical requirements demand transparency and maybe accountability. Such models make it easy for the process of configuring the data

The cons are…

• Dangerous privacy risks: Interpretable AI models may inadvertently expose sensitive information about individuals, leading to privacy breaches.
• Repeating and exacerbating human racism: If the training data used for an interpretable AI model contains biased or discriminatory patterns, the model may learn and perpetuate those biases.
• Causing mass unemployment as robots replace people: In certain industries, interpretable AI models may lead to job displacement as automation takes over human tasks.
• Lack of AI transparency and explainability: While interpretable AI models can provide insights into their decision-making process, they may not always completely understand how they arrive at specific conclusions.
• Social manipulation through AI algorithms: Interpretable AI models can be exploited to manipulate public opinion or behavior, potentially leading to social unrest.
• Social surveillance with AI technology: The interpretability of AI models can enable surveillance systems that infringe upon individuals’ privacy and civil liberties.
• Lack of data privacy using AI tools: Interpretable AI models may require access to large amounts of personal data, raising concerns about privacy and security.
• Biases due to AI: Interpretable AI models can inherit biases from the training data, resulting in unfair or discriminatory outcomes.
• Socioeconomic inequality due to AI: The deployment of interpretable AI models may exacerbate existing socioeconomic disparities by favoring certain groups or individuals.
• Weakening ethics and goodwill because of AI: The reliance on interpretable AI models can potentially erode ethical decision-making and human empathy.

Approaches to Improving Interpretability and Explainability

Visualization Techniques

Visualizing data and AI model workings can be a game changer in understanding complex processes. Think of using heat maps; they’re great for highlighting the features that an AI model prioritizes in its decision-making.

Decomposition Strategies

Getting to grips with AI models can be tough, but breaking them down into simpler components can help. Imagine taking a complex classification model and splitting it into easier-to-understand binary classifiers. It’s like disassembling a puzzle into smaller, manageable pieces.

Example-Based Explanations

Sometimes, the best way to understand an AI model is through real-world examples. Show how the model behaves in scenarios similar to the current input as if you’re using case studies to make sense of a theory.

Post-Hoc Analysis

After an AI model makes a decision, we have to unpack how it got there. Techniques like feature attribution come in handy here because they help identify which inputs were key in the model’s decision-making process, sort of like retracing steps to understand a journey better.