Understanding the black box: why explainable AI matters
Artificial intelligence has advanced rapidly, but one of its most consequential challenges remains the so-called “black box” problem. Dr. Sakib Mostafa, a Bangladeshi-Canadian researcher, frames this not as a sci‑fi nightmare but as a practical hurdle: the results that AI models produce can be correct yet inexplicable. In medicine, where lives are on the line, understanding how a diagnosis is reached is as important as the diagnosis itself.
From curiosity to impact: Mostafa’s journey
Mostafa’s fascination with AI began in childhood, shaped by cinematic depictions of machines and the curiosity to confront fear rather than flee from it. His studies at the University of Saskatchewan (USask) culminated in a PhD focused on explainable AI. Now a post‑doctoral fellow at Stanford University, he is applying those principles to detect cancer, aiming to merge interpretability with accuracy in high‑stakes settings.
The risk of opaque systems
Today’s AI tools—often built on deep learning—excel at pattern recognition but can operate as black boxes. Once data enters an AI model, the internal transformations become difficult to trace. This makes it challenging for clinicians to trust AI decisions if they cannot understand the underlying reasoning. Mostafa recalls an early project where a model analyzing plant-leaf images appeared to use multiple features, only to reveal that it was essentially relying on leaf edges. That moment underscored a broader issue: accuracy alone is insufficient; trust and transparency are equally vital.
Explainable AI in cancer detection
Mostafa’s work at Stanford’s Department of Radiation Oncology centers on building AI systems that can interpret complex, multi‑modal data—genomics, imaging, and clinical information—to diagnose cancer with confidence. The goal is not merely to detect cancer but to determine its stage and type, identify data-driven patterns, and reveal the rationale behind each decision. By incorporating explanations into models, the team can identify data that misleads the system and refine it accordingly.
A human‑inspired approach to machine intelligence
Mostafa likens AI tools to a skilled instrument: powerful in the right hands, dangerous if misused. “You cannot just go blindly using a tool,” he says. If a clinician is handed a surgical instrument without knowledge of its operation, the risk of harm increases. In AI, a transparent model offers a similar safeguard—prioritizing patient safety and clinical usefulness over sheer computational prowess.
Clinical potential and future steps
The long-term aim is to translate research into practice. Mostafa envisions a system that can be piloted at Stanford Hospital, offering clinicians support in diagnosing cancer and guiding treatment decisions. By understanding how the model reaches its conclusions, doctors can better trust and collaborate with the AI, validating its suggestions against established medical knowledge and patient-specific factors.
Why explainable AI could transform medicine
Explainable AI holds promise beyond cancer detection: it could revolutionize fields where data is diverse and high‑stakes. In law, finance, and public health, interpretable models help professionals verify results, identify bias, and refine tools to reduce errors. For cancer care, the payoff is measured in earlier, more accurate diagnoses and more personalized treatment plans that improve patient outcomes.
Looking ahead
Mostafa’s research aligns with a broader shift toward trustworthy AI in medicine. By prioritizing explainability alongside performance, his team seeks to unlock AI’s potential to augment human expertise rather than replace it. The end goal remains clear: a robust diagnostic system that understands its reasoning, supports clinicians, and ultimately saves lives.
