What is eXplainable AI?

XAI in Security and Surveillance: A Double-Edged Sword XAI in Security and Surveillance: A Double-Edged Sword Introduction Security and surveillance systems are increasingly powered by AI—whether in facial recognition, anomaly detection, or predictive policing. Here, XAI (Explainable Artificial Intelligence) plays a paradoxical role: it can strengthen accountability and fairness, but it can also expose system weaknesses to bad actors. Balancing transparency and security is the central challenge. Key Terms False Positive Rate (FPR) : The proportion of innocent people incorrectly flagged as threats. Threshold : The score cutoff where a model issues an alert. Fairness metrics : Ways of measuring equity across groups (e.g., equal false positive rates). Algorithmic Impact Assessment (AIA) : Pre-deployment review of risks, harms, and safeguards. Case Study A: Facial Recognition Facial recog...

Explainability in Self-Driving Cars: Lessons from Tesla and Waymo Explainability in Self-Driving Cars: Lessons from Tesla and Waymo Introduction Self-driving cars integrate perception, prediction, and planning to navigate complex road environments. But when accidents occur, one urgent question emerges: Why did the AI act that way? This is where XAI (Explainable Artificial Intelligence) plays a crucial role—helping engineers, regulators, and the public understand the decision-making of autonomous vehicles. Key Concepts Perception : Sensors like cameras, radar, and LiDAR detect objects and lane markings. Prediction : Estimating how other vehicles, cyclists, or pedestrians will move. Planning : Deciding the car’s own trajectory—when to brake, accelerate, or change lanes. Black box risk : Opaque models that prevent clear accountability after crashes. Tesla vs. Waymo: Different Design Philosop...

Healthcare AI: The Role of Explainability in Diagnostics — Industry Case Studies in XAI Industry Case Studies in XAI Glossary Audiences Case Studies Comparison Checklist Pause & Probe Blogger Series • Critical Thinking for Collaboration Healthcare AI: The Role of Explainability in Diagnostics XAI (Explainable Artificial Intelligence) refers to techniques that make AI models transparent , interpretable , and justifiable to humans. In healthcare, explainability is not a cosmetic add‑on. It is a clinical safety feature, an ethical commitment, and a legal anchor. When an algorithm influences a diagnosis, clinicians, patients, and regulators each need an explanation— but not the same one . In this article, we map the diagnostic AI landscape, distinguish the informational needs of different audiences, and walk through real‑world patterns for medical imaging , clinical decision sup...

  Industry Case Studies in Explainable AI (XAI) Industry Case Studies in XAI FinTech Healthcare Self‑Driving Cars Security & Surveillance Blogger Series • Critical Thinking for Collaboration Explainable AI (XAI): Four Industry Case Studies with Comparisons, Diagrams, and Critical Questions XAI (Explainable Artificial Intelligence) means techniques that make AI decisions transparent , interpretable , and justifiable to humans. Throughout, we refresh key terms and avoid jargon by spelling it out — e.g., LLM (Large Language Model) , ROC (Receiver Operating Characteristic) , AUC (Area Under the Curve) . Each article ends with Pause & Probe questions to train critical thinking. How FinTech Firms Use XAI to Build Trust FinTech (Financial Technology) products trade in one scarce commodity: trust . If an app manages your savings, approves your mortgage, or freeze...

  Can AI Explain Its Own Creativity? Emerging Frontiers Series Introduction: When Machines Surprise Us In 2023, a generative model shocked the art world by winning a digital art competition with a painting created from a short text prompt . The judges were impressed by its novelty and style—but when asked why the AI made the choices it did, there was silence. This raises one of the most provocative questions in modern AI research: If creativity means producing something both novel and valuable, can artificial intelligence not only create but also explain the “spark” behind its originality? Humans often explain their creativity by pointing to inspirations, constraints, or goals: “I chose this color palette because it reminded me of dusk in Tokyo ,” or “I used this metaphor to capture both freedom and fragility.” But can a machine, trained on vast amounts of data, provide an explanation that goes beyond pattern-matching ? Or are its “explanations” just post-hoc stories we wa...

  Neurosymbolic AI and the Future of Interpretable Reasoning Emerging Frontiers Series Introduction: Two Traditions, One Frontier For decades, AI has been pulled in two directions. Neural networks —flexible, data-driven models inspired by the brain—excel at pattern recognition but often function as inscrutable black boxes . Symbolic AI —systems built on explicit rules, logic, and reasoning—offers clarity but struggles with complexity and ambiguity. Now, a new field aims to merge the best of both: neurosymbolic AI . By blending the perceptual power of deep learning with the transparency of symbolic reasoning, neurosymbolic systems promise not only smarter AI but also more interpretable reasoning —AI that can both see the world and explain its inferences . But can this hybrid approach truly deliver on the dream of explainable intelligence? Or will it just make AI explanations sound more rational, while hiding complexity under the hood? Defining the Pieces: Neural, Symbolic,...

  XAI for Generative AI: Explaining Images, Text, and Code Emerging Frontiers Series Introduction: When the Machine Becomes a Creator In 2025, millions of people ask generative AI systems to produce new things every day—essays, poems, code snippets, business plans, paintings, and even full songs. These systems are astonishing, but they raise a new question: When an AI generates something original, what would it mean for it to explain how and why it made that choice? If a model writes a Python function to scrape a website, we may want to know which training examples inspired it. If it paints a picture “in the style of Van Gogh ,” we may want to see how it combined brushstroke patterns, colors, and visual motifs. If it writes a legal summary, we want to trust that it reflects accurate sources rather than free-flowing invention. This is the heart of XAI (Explainable AI) for generative models : moving from explaining decisions (why did the model classify this image as a cat?)...