Under the context of artificial intelligence, in the decision-making process of machine learning models, the absence of interpretability or transparency is referred to as a black box. This is the model where the inputs and outputs can be observed. However, the decision-making processes or the inside workings are not visible and understandable by humans. Regardless of their high levels of efficiency and accuracy, black box models are difficult to audit, explain or interpret. Hence, researchers are innovating transparent AI models in regard to these concerns.
Here’s what we’ll cover in the articles:
- What is Explainable AI?
- How does Explainable AI make Machine Learning Models Transparent?
- Explainable AI – Techniques
- Explainable AI – Real-life Applications
- Explainable AI – Benefits
- Making Machine Learning Models Transparent – Challenges Faced
- Master Explainable AI with Interview Kickstart!
- FAQs about Explainable AI
What is Explainable AI?
Even though artificial intelligence has entered every field or business, to rely unthinkingly on it for important decisions is not a good option. This is so, because it lacks reliability as the route of conclusion lacks transparency. As a solution, explainable AI has been innovated to gain transparency in its actions. It assists humans to achieve clear results from AI algorithms.
Explainable AI is also called XAI. When it is integrated with machine learning systems, the AI clearly explains the details of decision-making, indicating working mechanisms and its strengths and weaknesses. This increases transparency and reliability, helping humans to make better decisions.
How does Explainable AI make Machine Learning Models Transparent?
By providing explanations and insights on how a model reached its decision, explainable AI brings transparency to AI. The goal of explainable AI is to make the decision-making process of AI more understandable and clear to humans, as it is important in several real-world applications.
Explainable AI – Techniques
Listed below are a few techniques through which XAI achieves transparency in AI: :
Model Visualization
To help the user understand the process in which an AI model makes decisions, explainable AI uses visualization techniques. It explains how the model processes data, displaying relationships between multiple variables and weights assigned to each variable.
Feature Importance Analysis
Explainable AI helps to monitor the variables or features which are more important for decision-making. By analyzing the features that drive the decision, humans obtain insights into the underlying mechanism of any AI model.
Natural Language Explanations
To describe how an AI model reached its decision, explainable AI generates natural language explanations. It makes the decision-making process easily understandable to humans.
Counterfactual Explanations
For instance, if some variables are changed, explainable AI provides a ‘what-if’ situation showing how it might affect the decision. Through this technique, users can understand the sensitivity of the AI model by bringing changes in input data.
SHAP
SHAP, also known as Shapely Additive Explanations, is a technique that assigns values for the fair distribution of each feature’s contribution. It differentiates between baseline prediction and the model’s prediction. For example, through this technique, you can understand the reason behind the rejection or approval of a loan.
LIME
LIME, also called Local Interpretable Model-Agnostic Explanations, helps in creating interpretable and simpler models for obtaining approximate information about a complex model’s behavior at a certain instance. It is helpful to estimate the reasons for black-box models or certain predictions.
Rule-Based Models And Decision Trees
These techniques are often used to offer transparency by explaining the logic behind certain decision branches. They offer step-wise insights into how the model processes information for interpretation.
Attention Mechanisms In Deep Learning
This technique makes it easier to understand the inputs influencing the decision of AI.
Model Distillation
Under this technique, simple and interpretable models are trained to mimic the complex model’s behavior. It offers a simplified model that nearly approximates the decisions of the original model.
Prototype-Based Explanations
In this technique, the prototypes for every class are utilized to understand the reason behind the decisions. For instance, Prototype-Based Explanations help in prototype identification for different kinds of animals in order to explain the image classification of a model.
Hence, by using these AI techniques, the transparency of AI is increased, assisting humans to understand the AI model’s decision-making process better. It enhances the reliability and accountability of AI systems, creating better connections between AI and humans.
Explainable AI – Real-life Applications
Explainable AI can be used for multiple applications with efficiency in several sectors. Some of the real-world applications of explainable AI are listed below:
Medical diagnosis
AI models monitor medical images, helping doctors diagnose diseases. Its models highlight places of concern in the image and display how they occur at their diagnosis. Through this, doctors gain valuable insights into their decision-making process.
Fraud detection
AI models are used by financial institutions to catch fraudulent transactions. Investigators can easily understand why a certain transaction has been flagged as fraudulent, helping them make better decisions.
Natural language processing
AI models are built to analyze any text and extract valuable information from it. This helps researchers understand the process in which the model reached its conclusions.
Explainable AI – Benefits
Not only does explainable AI help in making AI models easily understandable to humans, but several other benefits come along. A few of these are mentioned below:
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Gain Trust
By clearly explaining the process of decision-making, XAI helps in increasing the trust of humans in AI systems. Once people understand the reasons behind a particular decision, they are more likely to trust AI systems.
Compliance
When in need of transparency and accountability for decision-making processes, XAI helps humans and organizations in following such standards and regulations.
Better Decision Making
When XAI offers humans insights into the AI model’s decision, humans can better understand the process and make better decisions. It even helps humans to analyze any errors in the model and correct them.
Making Machine Learning Models Transparent – Challenges Faced
Even after several techniques Offered by deep learning explainable AI covering multiple sectors, there are still multiple challenges that need to be overcome, such as:
- One of the major challenges of explainable AI is that it can offer explainability in AIÂ by compromising in model performance accuracy because AI systems have lower performance as compared to black box models.
- Explainable AI also faces challenges in generating understandable and correct explanations.
- As compared to the uninterpretable machine learning models, explainable AI models are more complicated to train and tune.
- As explainability features need human interference, it might be difficult to deploy AI systems.
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With Explainable AI, humans can acknowledge the process and reasons behind the decisions of a certain AI model. Moreover, once humans analyze the process, they can easily detect errors in the model, make corrections and enhance their decision-making process. Explainable AI has helped researchers and investigators to better understand AI models by making these machine-learning models transparent. Hence, researchers of every sector can now detect the errors with the reasons behind them and make informed decisions.
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FAQs about Explainable AI
Q1. What are the 3 stages of transparency?
The 3 stages of transparency are – opaqueness, transparency and clarity.
Q2. Why should AI models be transparent?
AI transparency ensures that all researchers clearly understand the working process of AI systems for better decision-making.
Q3. Why is transparency in machine learning hard?
Lack of standardized methods for accessing transparency makes it hard in AI. Moreover, you cannot rely on all the transparency methods, as they can generate different results every time.
Q4. What are the 3 levels of transparency in AI?
Algorithms, interaction and social are the 3 levels of transparency in AI.
Q5. What are the drawbacks of Explainable AI
Security, data privacy, AI model complexity, user understanding and human bias issues are some of the drawbacks of Explainable AI.
