Explainability: The foundation for Reliable, Actionable AI in Business

Explainability is the bedrock of reliable and actionable AI in business. Imagine an AI system predicting a surge in customer churn. While the prediction itself might be valuable, without understanding why the model predicts this, it's difficult to take informed action. For example, a Deep Learning model analyzes an image and predicts with 72% accuracy that a patient has lung cancer. Though the model might be correct, a doctor can't confidently advise a patient without understanding the reasoning behind the model's diagnosis.

The Power of Explainability:

Explainability demystifies AI decisions by translating them into business-understandable terms, fostering:

• Trust and Transparency: When executives understand the rationale behind AI recommendations, they're more likely to trust and adopt them, fostering openness throughout the organization.
• Actionable Insights: Explainability helps translate complex AI outputs into meaningful insights. By understanding which factors drive predictions, businesses can make data-driven decisions and optimize their strategies.
• Risk Mitigation: Unforeseen biases or limitations within AI models can pose significant risks. Explainability allows businesses to identify and address such issues before they impact operations or cause unintended consequences.

Let's revisit the customer churn example:

Without Explainability:

• The business might simply discount the prediction, lacking confidence in its legitimacy.
• Implementing a solution becomes challenging, as the root cause of the churn remains unclear.

With Explainability:

• The model reveals that customers who haven't used the loyalty program in six months are more likely to churn because they perceive a lack of value in their relationship with the company.
• This insight empowers the business to develop targeted campaigns that highlight the program's benefits and incentivize engagement, potentially mitigating churn.

Challenges and Approaches:

While seemingly simple, the more sophisticated an AI system becomes, the harder it is to pinpoint its reasoning.

• Machine Learning (ML) models: Some models, like Decision Trees, are inherently more interpretable, offering step-by-step explanations. In Machine Learning the Data Scientist plays an active role in feature engineering and training the model, so they have a higher ability to create explainability.? However, most ML models remain "black boxes."
• Generative AI (GenAI): Explainability in these models, designed for content creation, is even more complex due to their opaque nature. GenAI’s Foundation Models are pre-trained and are extremely complex.

Moving Forward:

As executives, we must require explainability as part of the outcome, whether from the model itself or through additional structures.

Decomposition and Context:

• Feature Importance Analysis: The model might identify features like "purchase frequency" as significant contributors. The explanation would highlight their relative importance (e.g., "purchase frequency" contributed 40%).
• Providing Context and Justification: The explanation could showcase historical data correlating low purchase frequency with churn and present specific customer examples supporting the prediction.

Human Involvement and Feedback:

• Human Validation: Customer service representatives can review flagged high-churn-risk customers and investigate their individual circumstances.
• Feedback Loop: Customer service feedback on the model's accuracy and any identified biases can be used to refine the model and improve future predictions.

These techniques showcase how explainability enhances the customer churn use case:

• Actionable Insights: By understanding key churn drivers, the business can develop targeted interventions.
• Transparency and Trust: Providing clear explanations fosters trust in the AI system.
• Continuous Improvement: Human feedback allows for continuous improvement, ensuring accurate and unbiased predictions over time.

Emerging Techniques:

Several promising techniques, like feature importance analysis , local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) are emerging to address the explainability challenge.

Conclusion:

Explainability empowers businesses to move beyond "what" the model predicts to "why" it makes those predictions. This enables them to make informed decisions, refine strategies, and achieve better business outcomes through responsible and effective AI utilization.

Link to previous chapters.