In a world where you can transform everything using artificial intelligence (AI) capabilities, think about a helping hand that helps you make the right decisions. Likewise, in the realm of computers and AI, smart programs make intelligent decisions; for example, they help recommend products. The concept of Explainable AI (XAI) comes into the picture now.
XAI focuses on the reasoning aspect behind the decisions made. However, we should understand that not all AI comes under explainable AI. Let’s discuss explainable AI in detail.
Explainable AI involves a series of methods that facilitate human users to understand how and why the AI has arrived at a particular decision or outcome. XAI assumes great significance as it has a key role in fairness, accountability, and transparency in the machine learning model. Explainable AI is ideal for building trust when you use AI. More importantly, XAI helps you understand and interpret the behavior of an AI model.
For example, there are issues like AI bias, where there is an anomaly in the results of the ML algorithm due to the prejudiced assumptions in the training data.
The explainable AI tools help users identify and reduce the issues of interpretability. The efficiency in interpretation enhances the trust in the ML model, particularly in credit business, law, and healthcare services.
Explainable AI uses several methods to explain the decision-making process behind the AI models. We shall discuss a few of them below:
Depict how an AI model processes the information/data. For example, the explanation graph shows the user’s purchase history if you use an AI model to recommend products and services.
Typically a flowchart-like structure, decision trees show you how the AI model has made a particular decision and the factors that influenced it while arriving at such a prediction.
As the name suggests, they explain why an AI model makes specific decisions. For example, if you use AI models for recommending products, local explanations show the products under consideration and the reasons for selecting them.
XAI is used in several industries, such as finance, healthcare, self-driving vehicles, and many more. Let’s briefly explain how XAI helps several industries in decision-making.
XAI helps detect financial discrepancies and sanctions or denies claims on loans, mortgages, and so on. On top of it all, they help predict market price fluctuations.
XAI helps diagnose patients and creates an environment of trust between physicians and the system. Further, it helps physicians understand how an AI model arrives at a diagnosis.
XAI helps explain autonomous driving decisions, particularly safety-related ones. Passengers can understand the reasons behind the decision-making while they travel in a self-driving car, feel much safer, and help understand the situations that can be handled.
XAI imparts interpretability and transparency to AI models. Its benefits include:
To improve the explainability of an XAI model, you need to pay heed to the data used for training. Therefore, at the design stage, the development team must decide the data details used for training an algorithm. They also need to check the authenticity of data, whether it is prejudiced, and, if so, what is needed to reduce the bias. Also, you need to remove irrelevant data to arrive at the most accurate predictions.
If you want to make the most of XAI for your business, consider focusing on the following key aspects:
Ensure your AI adoption is safe and honest and inspires other enterprises. You should set up your principles, and it’s the first step in your AI adoption.
Keep in mind that your AI strategy should match your business goals and the ways by which AI can help achieve them. Moreover, it should cover the ways to reduce expenses, improve productivity, methods to build AI capability, and the persons who can access the AI tools.
After you have specified how AI can assist your enterprise, identify its business applications. You can use it to automate business processes, make predictions, and generate marketing content.
Finally, you have to make all efforts to enhance the AI knowledge among your teams and the AI capabilities to explore its utility in the long run in your enterprise. You can help upskill your employees or hire experts with the needed skills, whichever is ideal.
When was explainable AI first introduced?
The Defense Advanced Research Projects Agency introduced XAI in 2017. It focused on developing AI systems that facilitate explanations for decisions made.
What exactly is explainable AI?
Explainable AI is a set of methods that helps users understand and trust the outcomes created by machine learning algorithms.
What is the objective of XAI?
The purpose of XAI is to explain the reasons behind the decision processes made by machine learning algorithms. It helps identify biased outcomes arising from a lack of quality in training data.
The adoption of AI and its usability may vary between enterprises and industries. However, laying the foundation is a big step toward adopting AI systems within your enterprise. In the first stage, you must decide on the ethics, objectives, use cases, internal skills, and capabilities. In any case, the adoption of technology requires adherence to best practices.
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