Explainable AI – Converting the black box of AI into a glass box

Exlpainable AI
Author : Sulakshana Iyer   Posted :

In this digital age, artificial intelligence (AI) solutions have drastically grown in popularity. The biggest advantage of leveraging AI in the business world is to make human decision-making process better with machine learning (ML) algorithms. However, with the growing popularity of AI, the types of decisions and predictions the AI-enabled systems make must become more insightful as in many cases, they are critical to business success, human life and personal wellness. And for companies to derive explainable and actionable information from huge amounts of data, it is essential to combine computing power and ML capabilities.

According to an annual survey by PwC, the vast majority (82%) of CEOs agree that for AI-based decisions to be trusted, they must be explainable.

Although, AI systems are powerful enough to decipher accurate answers, we need to know how has it worked. After all, it is not wise to blindly depend upon machine-made decisions, unless we know “how and why behind their decision making”. This is where explainable AI service, comes into the picture.

What is Explainable AI

Explainable AI (XAI) is an AI system that makes the entire decision-making process evident, understandable and fast. In other words, XAI removes the presumed black boxes and clarifies the estimations, insights, or predictions comprehensively explaining how the decision was made.

It is necessary to have clarification for AI-driven decisions because one wrong decision can lead to huge losses. Precisely, explainable AI for business gives decision-makers direct control over AI’s operations justifying their trust in the predictions. Hence, while building a robust explainable AI system or application, the questions given below must be considered –

  • Why should you trust the decision? 
  • Why did the model provide this decision?
  • In what way is the input converted to output?
  • what data sources to be leveraged?

An AI system is not only expected to perform a certain task or impose decisions but also has a model with the ability to give a transparent report of why it came to specific conclusions.

Insights

AI Maturity Framework for Organization

Artificial intelligence is considered to be the most significant technological advancement of this generation. And to thrive in this digital era, organizations need to become AI-driven and more digital. Moreover, it is imperative to realize the value that an AI maturity framework brings for companies. This makes it even more crucial for every business to assess their own attributes contributing to AI adoption and where they stand in the AI maturity journey.

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Reasons why Explainable AI is a must for today’s business

Explainable AI is going to be the mainstay of intelligent systems, empowering enterprises from various domains such as manufacturing, banking, healthcare, insurance, SCM and more to make fail-proof decisions. However, if we talk about explainable AI for healthcare, if the AI model fails to explain their reasoning then their decision or predictions will be questioned, disregarded or even overruled. As a result, the context, as well as the rationale of prediction such as medical diagnosis, treatment plans, automated prescription, could be dubious, disastrous and error-prone.

Enable precision in decision-making

It is impossible for an enterprise to optimize decisions that are not understandable. In case, you understand how and why a device derived a certain decision then it will become less complicated to enhance it via systematic experimentation. The greater explainable a model becomes, the better systematic its optimization becomes. With XAI, you get insights into the data, parameters considered and decision points utilized to make a particular suggestion. After all, XAI eliminates the alleged black boxes and clarifies broadly how the decision was made.

Justifies factors taken into consideration

Greater explainability not only helps in making smart decisions including enhancements to an AI model but also helps in making decisions related to the outputs of an AI model. For instance, if a machine learning (ML) system predicts 70% chances that a consumer is going to leave the brand, you may want to retain them with a retention discount or maybe the system recommends customer-centric option. But what if the customer’s motivating factor isn’t discount but the most recent records of client service experiences? Hence, understanding the factor influencing the overall decision is imperative to identify what can be done to prevent customers from churning out.

Enable impartial decision-making

As ML systems majorly depend on the data there are chances that it can make decisions susceptible to bias and manipulation. With explainable AI you can improve the system by training the model to adapt to data and make unprejudiced decisions.

To conclude

All in all, explainable AI aims at developing intelligent systems for businesses to provide them with crystal clear, human-understandable decisions that also explain why a certain AI model made a certain decision. Hence, when your organization maps out AI strategies, XAI should be the prime consideration to prevent illogical decision-making and maximize business value. If you want to know how XAI systems can provide transparent explanations for better decision-making, then contact our AI experts. 

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