Why We Need Explainable AI (XAI)

Explainable AI (XAI) is a next generation of artificial intelligence (A) that makes the logic for its recommendations easily accessible to users. Think of it as an open or “white box,” where decision logic is shared, and parameters can be tuned for more and more accurate results.

Contrast that with the “black box” of generative AI such as ChatGPT, Bard and more. All of these AI systems generate outputs without anyone knowing how they arrived at their specific results. It’s a closed, general type systems for broad use.

That may be fine for your sales and support agents, or your marketing personalization. It’s not acceptable for most complex analysis, where the decision tree is a critical part of the deliverable.

Take patient diagnosis. Would you want your doctor following ChatGPT’s treatment recommendation with no insights as to why it created that specific plan?  Nope.  Nor would you want to produce an autonomous vehicle without understanding every single aspect of the logic behind its design.

That’s the value of XAI.

Why We Need Explainable AI

Ideally, XAI will provide methods that produce more explainable models (the logic behind the decision) while maintaining a high level of learning performance, for example prediction accuracy.  Such an AI advancement enables human users to understand, trust, and manage the emerging generation of artificially intelligent solutions with confidence and transparency.

Explainable AI (XAI) offers substantial value in a number of ways:

1. Trust and Confidence: If a user (particularly in high-stakes industries like healthcare, pharmaceuticals, autonomous vehicles or finance) is making decisions based based on AI recommendations, they need to trust the system. When AI can provide clear explanations for its actions, businesses can understand and thus trust the system’s decisions.
2. Improving Decision-making: By understanding the reasoning of AI models, we can get insights that were not apparent before. This can lead to improved decision-making processes and better outcomes. It also helps us enhance our own logic and decision processes as we learn from the XAI.
3. Debugging and Improvement: If a model’s performance isn’t satisfactory, developers and data scientists need to understand why to improve it. XAI can help them understand why a model might be underperforming or making incorrect predictions, thereby facilitating model improvement. This drives the iterative performance enhancement organizations seek as they move to build their own specialized models for use cases tuned to their unique requirements.
4. Regulatory Compliance: In many industries, businesses are required to explain their decision-making processes. For instance, under GDPR, individuals have a “right to explanation” for automated decisions made about them. XAI can support organizations in meeting these regulations.
5. Risk Management: By understanding the logic behind how AI models make their decisions, organizations can better manage the potential risks associated with AI outputs and decisions.

Where Will XAI First Offer Value?

Explainable AI is particularly relevant in any type of analytics or empirically based decisions, where understanding the reasoning behind predictions or recommendations is critical. These are the same types of use cases where quantum computing is expected to shine, since it solves the processing challenges caused by the large and complex data sets used in analytics.  This is why many see the combination of quantum computers and explainable AI as a perfect and quite necessary match.

Following  are some likely use cases for XAI:

1. Credit Scoring: As an example of credit scoring, financial institutions use AI models to decide whether to grant a loan to an individual or not. These models take into consideration various features like income, credit history, employment status, etc. With explainable AI, it becomes easier to explain why a certain credit decision was made, which meets the transparency required by customers and for regulatory compliance. It also empowers financial firms to continually improve their decisions as they iterate and enhance the models.
2. Healthcare Analytics: Healthcare providers use AI models for treatment recommendations, patient risk scoring, and productivity. Explainability in these models can help doctors understand why a certain treatment was recommended, thereby increasing trust in the system and possibly uncovering new medical insights.  It’s even possible that these systems can become so accurate and trusted that they drive patient diagnoses.
3. Customer Segmentation: Businesses use AI models to segment their customers based on their behaviors, preferences, and other features. Explainable AI can provide insights into why certain customers are grouped together, enabling businesses to tailor their marketing and sales strategies  more effectively.
4. Predictive Maintenance: Companies use AI models to predict when a piece of equipment might fail. With XAI, they can understand why the model thinks a failure might occur soon, enabling more targeted maintenance efforts. Planned maintenance reduces costs while improving overall productivity and ROI.
5. Fraud Detection: In finance and insurance, AI models are used to detect fraudulent transactions. With explainable AI, investigators can understand why a certain transaction was flagged as fraudulent, facilitating the investigation process. It can also build segmentation and profiles of fraud to guide users to better recognize fraud in action.
6. Risk Management: Companies use AI models to manage various types of risk, such as credit risk, market risk, operational risk, etc. XAI can provide insights into why a certain situation is flagged as high risk, enabling better risk mitigation strategies and their execution.
7. Churn and Upsell Prediction: Businesses can use AI models to predict which customers are likely to stop using their products or services, or which customers are primed for an upsell or a new product. With explainable AI, they can understand the reasons behind predicted customer churn and opportunity, which can be used to create sales and marketing strategies to retain  or expand those customers.
8. Supply Chain Optimization: Companies use AI models for demand forecasting, inventory management, and route optimization in their supply chains. XAI can provide insights into these predictions and optimizations, leading to better decision-making, smoother supply chain operations and lower cost.

In all these use cases, the key value of XAI is to make AI decisions transparent, understandable, and hence actionable, leading to better business decision-making.

The Bottom Line

Explainable AI is an active area of research, with the goal of increasing AI transparency without significantly sacrificing performance.  That’s the big challenge today.  AI takes enormous processing power to generate models and outputs. Only the largest systems are capable of running today’s AI, even at a generic level. And it’s expensive to process. So there are opportunities.

Explainable AI will come to fruition. The value it will deliver is significant in high stakes industries where complex analysis is at the core of critical decisions.