Explainable artificial intelligence (XAI) is 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 is used to describe an AI model, its expected impact, and potential biases. It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision-making. Explainable AI is crucial for an organization to build trust and confidence when putting AI models into production. AI explainability also helps an organization adopt a responsible approach to AI development.
What is Explainable AI and its Meaning?
Explainable AI (XAI) is a type of AI system that is specifically designed so that it can be easily understood by humans. AI can be seen as the recent tool that enables experts to improve the effectiveness of interactions with customers through the interpretation of designed AI systems’ decision-making algorithms and pre-temporal decision-making. While traditional models often operate as a “black box” with many of the working unknown they can still be used to make decisions that the AI model provides inputs on how it came up with such decisions.
Different types of methods are used to explain the machine learning model, like some model-specific methods or specific algorithms. The highest level of success is when the AI technique is sophisticated and, at the same time it is understandable and can assist in the decision-making process.
How Explainable AI Works with Example
Explainable AI is the idea of creating AI systems that would be able to explain their decision-making process. Writing in such a manner also provides users the ability to comprehend, accept, and thereafter utilize AI systems. To better understand this concept let us try to conceptualize a real-life situation.
What if an AI system is incorporated into diagnosing patients’ medical conditions? This system takes patient characteristics and uses them to estimate the risk of a disease. It might choose the treatment and offer it to the doctors without explaining how; in the earlier ‘black box’ model. Such a situation will not work effectively, especially when it comes to arriving at a critical decision where the rationale behind the decision has to be explained.
While with Explainable AI the same system would return not only the prediction but also the explanation as to why this prediction was made. For instance, the AI may explain that it deduced the forecast based on particular attitudes found in a patient’s laboratory test results, medical history, and signs. This could show what aspects (e.g., high blood pressure, discordant blood tests) played a very important role in the prediction.
This explanation assists the doctor in comprehending why the particular AL decision was made, in checking its correctness, and in making subsequent decisions on the patient’s treatment. Let us see how the global explainable AI market has grown over the years by the following table:
| Year | Global AI Market |
| 2023 | 6.4 |
| 2026 | 10.6 |
| 2029 | 17.6 |
| 2032 | 29.3 |
In summary, Explainable AI refers to the development of AI systems that should be able to explain their decisions to acts or events. Such openness leads to credibility, and errand responsibility, and enables the users to have proper decision making.
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Step by Step Guide as to how Explainable AI works
Let’s see how Explainable AI works step by step:
Step 1: Data Collection and Preprocessing
- Collect Data: Gather relevant, diverse datasets.
- Clean Data: Remove duplicates and handle missing values.
- Preprocess Data: Standardize and normalize the data.
Step 2: Model Selection and Training
- Select Model: Choose an appropriate algorithm.
- Train Model: Feed data into the model, allowing it to learn patterns.
- Evaluate Model: Use a validation dataset to ensure performance.
Step 3: Explainability Techniques
- Model-Agnostic Methods: Apply techniques like LIME and SHAP to explain any model’s decisions.
- Model-Specific Methods: Use methods tailored to specific algorithms, like decision trees for rule-based explanations.
Step 4: Generate Explanations
- Feature Importance: Identify key features influencing predictions.
- Visualization: Create visual aids like heat maps and plots.
- Natural Language Explanations: Provide understandable text descriptions.
Step 5: Interpretability Interface
- User-Friendly Interface: Develop clear, accessible interfaces for users.
- Customization: Tailor explanations to different expertise levels.
Step 6: Validation and Testing
- Validate Explanations: Ensure explanations are accurate and reliable.
- User Feedback: Collect and incorporate user feedback.
Step 7: Deployment and Monitoring
- Deploy Model: Integrate the model into production.
- Monitor Performance: Continuously check the model and explanation quality.
Step 8: Ethical and Regulatory Compliance
- Ethical Considerations: Ensure fairness and transparency.
- Regulatory Compliance: Adhere to laws like GDPR for automated decision explanations.
Also read: Who is the Father of Artificial Intelligence (AI)?
Principles of Explainable AI
There are various principles that are necessary for an Explainable AI. They are :
- Transparency: The first principle of Explainable AI is to be transparent. This helps to make the AI systems clear and easy to understand.
- Interpretability: It is one of the important principles of Explainable AI as it helps to understand how much an AI can be interpreted.
- Fairness: Explainable AI should assay to be fair and free of bias. This entails the identification and elimination of different prejudices within the data and models. The second aspect of fairness is the provision of output justification that does not contain discrimination-related factors and a guarantee of equal treatment of all people by AI systems.
- Accountability: This implies that organizations that integrate Artificial Intelligence must assume the consequences and have structures that can address the mistakes or negative impacts made by the AI systems. It also consists of allowing the users to challenge the AI decisions and explaining how this can be done in a precisely defined manner.
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- User-specific Design: Explainable AI should be user-centric. For example, it encompasses the aspect of strategizing the type of explanation that is to be given to the users depending on their understanding of certain concepts, and ensuring that the explanations that are given bear some level of significance and relevance to the users. For example, a doctor may need precise information about clinical recommendations, which are different from the particulars a patient would demand.
- Trust: Increase in faith and trust on AI models.
- Robustness: The developed AI systems should also be highly reliable, producing sound and stable explanations even in the condition when the system is attacked or when it is given inputs that are unknown to normal functionality. Representativeness makes sure that the explanation is still viable and dependable in varying circumstances.
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Examples of Explainable AI
Some common examples of Explainable AI in various sectors are:
- Healthcare: The concept of Explainable AI is applied in disease diagnosis for doctors and prescription advice. For instance, an AI system that aims at diagnosing diabetic retinopathy using images of the eye can explain its results by drawing the user’s attention to regions of the image that show the disease. This in a way assists the doctors in confirming the AI diagnosis and then in making proper treatment plans.
- Finance Sector: Explainable AI being applied in the financial sector in credit scoring and in detecting fraud. A credit scoring AI model can explain how it comes to conclusions regarding the approval or rejection of loans and the criteria used for the process, which can include credit history, income, or other debts on the applicant. This transparency assists the applicants in appreciating their creditworthiness hence enhancing it.
- Legal Sector: The AI system is being used in various judicial systems for assessing the risks and even sometimes before adjudicating as well.
- Customer Service: Nowadays, almost every other e-commerce platform uses AI as its chatbots or virtual assistants.
Conclusion
Explainable AI is emerging as a vital attribute to creating AI systems that are reliable and understandable. Therefore, by increasing the interpretability of AI models, it will be possible to increase the level of their responsible application. Healthcare, financial, security, and customer service industries have shown that explainable AI is effective and needful. Over the years, there have been gradual advances made in AI and it has now come to light that Explainable AI will be very important in promoting ethical practice in the use of AI.
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