Explainable AI: Unlocking the Black Box with Real Case Study
Artificial Intelligence (AI) is powerful, but it often behaves like a mysterious black box. It gives predictions—but never tells us why.
Imagine a factory system that alerts: “Break coming in 30 minutes.” The operator stares at the screen, wondering: Why? Which part of the machine? What can I fix?
That’s where Explainable AI steps in. Unlike traditional models, Explainable AI doesn’t just predict—it explains. In this blog, we explore what Explainable AI is, why it matters, and how it’s making a real-world impact—from predicting paper sheet breaks in manufacturing to demand forecasting and healthcare diagnosis.
What Is Explainable AI?
Explainable AI includes techniques that make AI decisions understandable to humans. Instead of blindly trusting predictions, Explainable AI allows us to see how and why decisions are made.
In one implementation using our BIG-AI platform, the system didn’t just say “A sheet break is likely in 30 minutes.” It also pinpointed why—highlighting how vacuum, pressure, and flow variations contributed to that prediction.
Why Is Explainable AI Important?
Explainability builds trust. Doctors, bankers, and engineers can’t just accept results—they need to understand them. Without explainability, AI adoption slows and accountability fades.
In papermaking, for example, AI can detect hidden patterns—tiny micro-drifts invisible to the human eye—and predict sheet breaks 20–30 minutes in advance. With explainability, operators know which parameters to adjust—vacuum, torque, or flow—transforming a cryptic alert into actionable insight.
That’s the difference between a black box and a glass box.
Explainable AI Techniques
Techniques like LIME and SHAP decode complex models by showing which factors influenced predictions. These tools turn AI’s black box into a glass box of insights, revealing how different inputs shape decisions.
Real Causes of Sheet Breaks
Sheet breaks occur due to multiple subtle interactions:
Vacuum & Dewatering Instability: Fluctuating couch vacuum or suction roll pressure weakens the sheet.
Press Section Torque & Load: Uneven load stresses fibers.
Stock Flow & Consistency: Variable chest levels create thin spots.
Chemical & pH Drift: Incorrect dosage or pH reduces fiber bonding.
Dryer Section: Uneven drying makes sheets brittle.
Reel Section: Speed mismatches cause tears.
Using BIG-AI, every parameter is monitored every 5 seconds—offering 24/7 machine “fingerprints” for predictive maintenance.
How Explainable AI Solves It
In one mill, operators repeatedly faced breaks during Shift A. BIG-AI identified the main culprits:
Vacuum instability (42%)
Press torque spikes (31%)
pH drift (15%)
Flow inconsistency (12%)
During Shift C, these parameters stayed stable—and no breaks occurred.
This clarity helped operators take action—stabilizing vacuum, balancing press load, and adjusting chemicals. What was once a warning became a road map.
Real Example: Retail Demand Forecasting
In retail, explainable AI predicted demand surges two days before weekends. The explanation?
Weather forecast: 40% influence
Promotional discounts: 35%
Local events: 25%
Managers used this transparency to restock intelligently—reducing waste and boosting profits.
Case Study: Healthcare Diagnosis
In healthcare, explainable AI helps doctors trust AI-assisted diagnosis. For example, when predicting heart disease risk:
Cholesterol levels: 45% influence
Blood pressure: 30%
Family history: 25%
Such breakdowns transform AI from a black box to a trusted diagnostic partner.
From Black Box to Glass Box
From factory floors to hospitals, Explainable AI is reshaping decision-making. Our journey with BIG-AI proves that predictive systems can also be transparent and trustworthy.
But explainability comes with trade-offs—balancing accuracy vs. interpretability and navigating complex deep-learning models. As regulations tighten, Explainable AI will become essential for ethical and responsible AI deployment.
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References
Arrieta, Alejandro Barredo, et al. “Explainable Artificial Intelligence: Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.” Information Fusion, vol. 58, 2020, pp. 82–115. Elsevier.
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. “‘Why Should I Trust You?’ Explaining the Predictions of Any Classifier.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
Lundberg, Scott M., and Su-In Lee. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems, vol. 30, 2017.
Holzinger, Andreas, et al. “What Do We Need to Build Explainable AI Systems for the Medical Domain?” arXiv preprint arXiv:1712.09923, 2017.
IBM. “Explainable AI: Building Trust in AI Systems.” IBM Research, 2021.
