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ExplainableAI (XAI)
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ExplainableAI (XAI)

Last Updated on July 24, 2023 by Editorial Team

Author(s): Data Science meets Cyber Security

Originally published on Towards AI.

Now Algorithms know what they are doing and why!


Hello, technophiles and curious minds. Welcome to the next chapter of the book Artificial Intelligence. Let us go further into the enigmas of Artificial Intelligence, where AI is making waves like never before! Artificial intelligence has successfully captured the attention of all generations, from Gen Alpha through Gen-Z, and even Boomers. It’s like gathering a generation to experience its fantastic universe while also revolutionizing businesses left, right, and center. It’s like an adrenaline rush that you can’t get enough of.


Have you ever become bored of hearing or assuming that Instagram understands what we were talking about and starts displaying you the photos or the precise sort of things you were discussing with a friend? But there is no specific response to this topic every time it arises. Perhaps the internet can state such and such, but what is true? And, even if we believe the internet, where is the explanation? We are human, and unlike our legal system, we require evidence to believe anything. So, don’t worry, this is where Explainable AI, also known as XAI, comes in.

In this blog, we’ll get headlong into XAI theory, expose its nuts and bolts, investigate numerous methodologies, and, of course, offer a variety of relevant and interesting examples that will make the magic of XAI crystal evident! So strap in, because we’re about to start on an exciting voyage that’s both technically sound and a lot of fun! Let us bring clarity to the field of artificial intelligence one explanation at a time! U+1F310U+1F9E9





Consider the following scenario: you wish to apply for a loan (be it a home loan, a vehicle loan, an educational loan, or anything else), and you are interacting with an AI system for the application process. Because that is what will occur in the next five years. You were turned down for a loan without explanation. Exactly! Sounds crazy to me as well, but here is where XAI comes into play, as it can assist you understand why you were refused and expose the specific reasons why, ensuring you don’t feel like you’re talking to a stone-faced and cold-hearted loan officer.



Let’s go through some instances to help you understand why Explainable AI is so important: Imagine a healthcare system in which, instead of speaking with a doctor, you interact with an AI system to assist you with diagnosis. Crazy, I know no one would trust the AI, not even myself! Reason: There would be no explanation for the recommended medications, which is suspicious. However, with XAI, that anxiousness may be replaced with comprehension! The system will offer the medical facts and explanation behind its suggestions, so you won’t be left in the dark, wondering if you’re in one of the black mirror episodes!



Let’s speak about something that every individual on the planet has experienced at least once in their lives. Have you ever binge-watched a show on Netflix or any other streaming provider and then received weird suggestions for a series you hadn’t even considered watching? Does it feel like our own electronics are snooping on us? Explainable AI can join the watch party and also assist you understand why the algorithm thinks you’d appreciate that criminal thriller or rom-com you’ve never heard of. So no more mystery algorithms playing sneaky and delivering recommendations you didn’t want.

Investing and Trading WITH AI:


The last example is intended for traders and investors. Consider someone who is a smart investor attempting to deal with the stock market. Because that individual is already knowledgeable, he or she is already using an AI-based trading system, but it seems more like a game of darts in the dark. You don’t know if you should trust that AI-powered system or not. But, fear not, my fellow investors and traders, explainable AI is here, and it can really explain the causes behind predictions, so you’re not simply blindly following its whims.

As we all know, every story has both good and bad sides, and AI is no exception. However, we have the ability to turn the bad into the good, which will eventually make AI less of a distant and unapproachable thing in which people lack trust, and more likely a truly understandable AI that will assist us in finding solutions to our problems. Essentially a human clone, but in the form of an AI


SOURCE: By Author

A. What is a black box in the AI model?

Okay, in very simple terms, picture a machine where you enter an input and receive an output (correct result), but you don’t know what’s going on within the algorithm since it’s all black, or think it like a digital Pandora box where the magic happens! This concealment is intriguing, but it doesn’t go far when it comes to dealing with the significant issues we discussed previously. I’ll give you an example: picture asking an AI assistant a severe inquiry about healthcare or whatever and getting nothing but a weird happy emoji! Isn’t that Strange? That is what I am talking about.

B. Limitations of black box models and the need for explainability

BLACK BOX models may also be effective when it comes to making predictions or conclusions without providing any inside knowledge, causing them to be perplexed as to how they arrived at that conclusion.

Let me give you some instances so you understand what I’m talking about

Deep Neural Networks (DNNs): about the course, you’ve heard about this one causing a stir among the techies. So, in general, Deep Neural Networks are incredibly complicated structures with thousands, if not millions, of linked nodes, which are difficult to grasp due to their enormous number of parameters and non-linear transformations.

Basically, determining which specific input leads to which linked nodes among millions of interconnected nodes to produce that specific output can be challenging, which summarises it as a classic example of the Black Box Model.

So, when it comes to these models and their complicated behaviors, which involve complex mathematical formulae and transformations that are nearly difficult for the human brain to grasp, but at the same time they are the most powerful and accurate models in the history of AI. However, we cannot trust them when it comes to the integrity and explainability of the numerous duties highlighted above. Here’s why we need explainability in every AI we deploy to keep consumers’ confidence, integrity, and authenticity.

C. Key concepts in XAI?


Well, the key principle to remember when dealing with Explainable AI is that it focuses on developing machine learning models, specifically algorithms that can give us reasons and explain why and how we got the result. The key concepts are as follows:

  1. INTERPRETABILITY: Consider this concept to be something that human minds can grasp. This implies interpretability primarily relates to the algorithm’s ability to describe how it works with crystal-clear explanations. It also assists users in gaining insights into the model’s technique of predicting results.
  2. TRANSPARENCY: Transparency simply refers to the word itself, which implies that the inner workings of the AI model should be easily accessible and transparent to users.
  3. LOCAL VS GLOBAL EXPLANATION: Let me explain in simpler terms how these two go hand in hand. Local explanation refers to the model’s prediction behavior, which is the last component, whereas global explanation refers to the model’s entire behavior, which includes everything from the beginning to the conclusion.
  4. HUMAN-COMPUTER INTERACTION: Communication is essential, whether it be between humans or between humans and computers, or vice versa. Essentially, this means that the user interface should be understandable and simple to use for those who have no prior experience with apps or even computers.


Let us explore some of the powerful decision-making approaches of AI, such as LIME, SHAP, and DeepLIFT, which are primarily and only aimed to shed light on the mysterious workings of AI models.

  • FEATURE IMPORTANCE: So this strategy purely assists us in determining which attribute has the most influence on the model’s predictions. Most variations strategies emphasize SHAP (SHAPLEY ADDITIVE EXPLANATIONS) and LIME (LOCAL INTERPRETATBLE).
  • LIME (LOCAL INTERPRETATBLE MODEL-AGNOSTIC EXPLANATION): Consider this strategy to be the ultimate spy that knows everything that is happening out there. On a wide scale, this strategy allows us to decode the black box model using a much simpler model (for example, linear regression). It causes disruption in the presence of a certain sort of data, then sits back and observes the changes made to the predictions, notes them, and then provides us with all the information and insights into the model’s behavior.
  • SHAP (SHAPLEY ADDITIVE EXPLANATION) VALUES: It is a notion derived from game theory that simply implies that it examines each and every characteristic of the model and notes which features contribute the most to the forecast. It computes a feature’s average marginal contribution over all available feature subsets.
  • RULE-BASED METHODS: Decision trees and rule-based models are understandable by definition. They can give clear choice routes which contribute to a certain forecast.
  • Visualization Techniques: Graphs, heatmaps, and saliency maps may be used to show how various features impact predictions, thus rendering it simpler for individuals to understand the model’s behavior.
  • Counterfactual Explanations: These strategies provide alternate input circumstances, which result in distinct model predictions, letting people comprehend “what-if” scenarios.
  • Concept-based Explanations: This entails converting model predictions into concepts that humans are able to comprehend. In the analysis of images, for example, it might emphasize items or regions that are intriguing that assisted in a certain prediction.
  • Layer-wise Relevance Propagation (LRP): LRP is a deep neural network explanation approach. It provides significance rankings to the output generated by every neuron, reflecting its impact on the final prediction’s accuracy.
  • Attention Mechanisms: Attention processes can be visualized in models such as the converters to comprehend which elements of the input are most crucial for creating a specific outcome
  • Model Distillation: This entails training a simpler model (such as linear regression or decision trees) to imitate the behavior of a more complicated model. The refined model can then be utilized to provide explanations.



Let us first discuss why it is critical to discuss bias and fairness in AI. Let’s look at a recent case study to get a sense of what this issue entails: I’m sure you’ve heard about the disturbing case of AMAZON’S AI RECRUITING TOOL, which was discovered to be biased against female applicants. Another case study includes racial bias in healthcare ALGORITHMS, which affected many black patients, ultimately resulting in racism in healthcare departments.

So now we know that every action has a consequence, and AI is no exception; resolving such consequences should be our top concern. Because, in the end, AI should assist us in making our lives easier rather than turning us against one another through its bias mechanisms. We now see why it is vital to address the system’s bias. Now, let’s look at the several sorts of bias in AI, as well as decision-making fairness and the approaches used to combat prejudice.


A. Understanding the bias in AI Systems:

In the field of artificial intelligence, it’s critical to confront the elephant in the room: prejudice. AI systems are only as good as the data provided to them, and regrettably, biased data may infiltrate and influence their choices. Bias may lead to wrongful discrimination and contribute to the perpetuation of societal inequities. Understanding the presence and impact of bias in AI systems is the first step toward developing models that are fair, transparent, and ethical. It’s past time to probe the underlying workings of artificial intelligence to assure justice and accountability in every byte of code.

Let’s address some of the most common types of bias that are more likely to occur and are often interrelated to each other:

  1. ALGORITHMIC BIAS: This sort of prejudice happens when the algorithm is purposefully or accidentally biased. For example, an algorithm trained on a dataset skewed towards males is more likely to produce skewed predictions about men.
  2. DATA BIAS: This form of bias happens when the data used to train the algorithm is skewed. If a dataset is biased towards white individuals, for example, the algorithm is more likely to produce biased predictions about white people.
  3. SOCIETAL BIAS: Societal bias: This sort of prejudice happens when the algorithm mirrors societal biases. If there is a social bias towards women in STEM disciplines, for instance, a system learned from data from STEM graduates is probably more inclined to produce biased judgments towards women.

B. The importance of fairness in decision-making:

Fairness is essential in AI decision-making. Fairness indicators such as equal entry into the workforce, same reliability, and ethnic equality can aid in identifying a potential bias. To assure equal results along with avoiding biases, fairness-aware algorithms, and regularisation approaches can be used.


XAI in Social Media: Understanding AI Algorithms Behind News Feed Recommendations:

In this fast-paced world where everyone enjoys social media, we cannot deny the fact that it is also a dark place where we share our personal information as well as information about our family and friends, and the craziest trend these days is artificial intelligence. . algorithms. We can figure out their decision-making process and explore how explainable AI brings transparency to this case for us:

Let’s start by understanding the Algorithm behind social media first:

  1. DATA COLLECTION: Data Collection: Social media networks amass massive quantities of user data, such as conversations, preferences, and engagement metrics. This information serves as a basis for the AI algorithm.
  2. DATA PREPROCESSING: Data preparation entails sanitizing and altering raw data to make it appropriate for analysis. This stage reduces noise and improves the algorithm’s capacity to detect patterns.
  3. FEATURE EXTRACTION: AI algorithms extract information such as user preferences, information relevancy, and past interaction from extracted data. These characteristics are fed into the recommendation algorithm.
  4. DESIGNING THE MACHINE LEARNING MODEL: Advanced machine learning methods, such as collaborative filtering, content-based filtering, and hybrid techniques, are used by the AI system. To locate relevant information, these algorithms analyze user traits and compare them with similar profiles.

We understood how the algorithm is been designed for any social media now how this algorithm actually works? Let’s go with the Explainable AI U+1F44D


  • XAI approaches can create rule-based explanations, disclosing specific requirements that the algorithm employs to propose material. For instance, “Posts are recommended based on similar user interactions and content relevance.”
  • Feature Attribution as we saw above — XAI supports feature attribution, which highlights the features that have a substantial effect on the suggestions. “Posts from friends with high interaction rates, for example, receive priority.”
  • User-Level Explanations: XAI clarifies why certain material appears on an individual’s feed by offering user-level explanations. For example, “This post has been recommended to you because of your interest in technology and recent engagement with similar content.”



XAI is crucial in bridging the gap between AI algorithms and consumers. It creates confidence and enables users to arrive at knowledgeable choices by offering detailed descriptions of why information is suggested on social media news feeds. As social media evolves, XAI’s openness offers a more personalized and user-focused approach while protecting against any biases and supporting a responsible AI community.


When delving into the realm of complex topics like Explainable AI, theoretical knowledge alone can only take learners so far. The true understanding and mastery of Explainable AI come from the invaluable experience of hands-on projects. These projects serve as the key to unlocking practical insights that textbooks and lectures often fail to convey. ProjectPro — a revolutionary learning platform designed to propel your knowledge from theoretical to practical realms. ProjectPro is not your typical online learning platform; it goes beyond lectures and assessments to offer a hands-on, immersive experience through a carefully curated selection of projects in various domains.

ProjectPro offers an extensive array of real-world projects that challenge learners to apply AI and machine learning concepts to tangible scenarios. By actively engaging in these projects, learners develop problem-solving skills and gain the confidence to tackle real-world challenges.

[Visit the website to know more about them- ]

Start learning about explainable AI methods. No matter who you are. Here are some recommendations to get you started:

EXPLAINABLE AI (XAI) — UDEMY COURSE: Covers everything right from basics to advance level of techniques used in explainable AI. Also contains real-life case studies to get the proper insight for the same.

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Covers a wide range of topics related to explainable AI, including the different techniques for explainable AI models, ethical and legal implications, and real-world case studies

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EXPLAINABLE AI WITH PYTHON: BY IBM ON COURSERA: This course teaches learners how to use Python to explain AI models on subjects such as feature extraction, counterfactual explanations, and model introspection.

[link: ]

EXPLAINABLE AI (XAI) COURSE — BY DATANIGHTS IN COLLABORATION WITH THE MICROSOFT REACTOR: This course covers both theory and practicals and uses cases of explainable AI. You’ll learn not only to generate the explanations of AI but also how to effectively communicate those explanations to stakeholders. A must look into course according to me.

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