This article explains what explainable AI (XAI) is and why it matters for businesses and society. It also provides some examples of XAI

How Explainable AI Can Boost Trust and Innovation in Business

Artificial intelligence (AI) is transforming every aspect of business, from customer service and marketing to product development and operations. But as AI becomes more complex and autonomous, it also becomes more opaque and unpredictable. How can we trust an AI system that can't explain its decisions and actions? How can we ensure that it aligns with our goals and values? How can we debug and improve it when something goes wrong?

These are some of the questions that motivate the field of explainable AI (XAI), which aims to provide transparency and accountability for AI systems. XAI, is a type of artificial intelligence that can explain its decisions and actions to human users. XAI is important for building trust, transparency and accountability in AI systems, especially when they are used for high-stakes or sensitive tasks. XAI can also help users to gain insights, feedback and improvement from AI systems. XAI is not only a matter of ethics and compliance, but also a source of competitive advantage and innovation for businesses.

Some of the methods and techniques that XAI uses to provide explanations include:

  • Feature importance: This method shows how much each input feature contributes to the output of the AI model, such as a score or a prediction. For example, an AI model that predicts the risk of diabetes can show how much each factor, such as age, weight, blood pressure, etc., influences the risk score.
  • Counterfactuals: This method shows how the output of the AI model would change if some of the input features were different. For example, an AI model that rejects a loan application can show what changes in the applicant’s data would lead to an approval.
  • Decision trees: This method shows the logic and rules that the AI model follows to reach a decision or a prediction, using a tree-like structure. For example, an AI model that classifies images can show the criteria and thresholds that it uses to identify different objects or categories.
  • Natural language generation: This method uses natural language to explain the output of the AI model in a way that is easy to understand for humans. For example, an AI model that generates captions for images can explain how it recognizes and describes the content of the image.
  • Visualizations: This method uses graphs, charts, maps or other visual elements to illustrate the output of the AI model and its underlying factors. For example, an AI model that detects anomalies in data can highlight the outliers and their causes.

According to a recent survey by IBM, 91% of businesses say that the ability to explain how AI arrives at its decisions is critical for adoption. Moreover, 68% of businesses say that XAI can help them gain insights and improve their processes, products and services.

Here are some of the benefits of XAI for businesses:

  • Enhance customer trust and loyalty: Customers are more likely to trust and engage with an AI system that can explain its recommendations, predictions and actions in a clear and understandable way. For example, an online retailer can use XAI to show customers why a certain product is recommended for them, based on their preferences, behavior and feedback. This can increase customer satisfaction, retention and loyalty.
  • Reduce risks and liabilities: Businesses can use XAI to monitor and audit their AI systems, ensuring that they comply with regulations and ethical standards. For example, a bank can use XAI to verify that its AI system for credit scoring does not discriminate against any group of customers, based on their gender, race or other attributes. This can reduce the risks of legal challenges, fines and reputational damage.
  • Foster innovation and collaboration: Businesses can use XAI to gain insights and feedback from their AI systems, enabling them to improve their performance and outcomes. For example, a manufacturer can use XAI to understand how its AI system for quality control detects defects and errors in its products, enabling it to optimize its production process and design. This can foster innovation and collaboration among different stakeholders, such as developers, engineers, managers and customers.

Here are some of the use cases where XAI can help businesses to understand, trust and improve their AI systems:

  • Insurance companies using ML models to set insurance rates: XAI can help insurers to explain how their ML models calculate the premiums and payouts for customers, based on their data and features. This can increase customer satisfaction and retention, as well as reduce the risks of fraud and error.
  • Educational institutions using ML to accept or reject applicants: XAI can help schools and universities to explain how their ML models evaluate the academic performance and potential of applicants, based on their data and features. This can increase student trust and engagement, as well as reduce the risks of bias and discrimination.
  • Manufacturers using ML models for quality control: XAI can help manufacturers to explain how their ML models detect defects and errors in their products, based on their data and features. This can increase product quality and customer satisfaction, as well as foster innovation and collaboration.
  • Retailers using ML models for product recommendation: XAI can help retailers to explain how their ML models suggest products for customers, based on their data and features. This can increase customer satisfaction, loyalty and sales, as well as provide insights and feedback.

XAI is not a one-size-fits-all solution, but rather a context-dependent and user-centric approach. Different types of AI systems may require different levels and methods of explanation, depending on their complexity, purpose and impact. Similarly, different types of users may have different needs and expectations for explanation, depending on their background, role and goal.

Therefore, businesses should adopt a holistic and human-centric strategy for XAI, involving all relevant stakeholders in the design, development and deployment of their AI systems. By doing so, they can leverage the power of AI while ensuring its trustworthiness and value.