5. Industry Case Studies

 

5. Industry Case Studies

Series: Industry Case Studies in Explainable AI (XAI)

Article 1: How FinTech Firms Use XAI to Build Trust

Key Idea: FinTech (Financial Technology) firms operate in a domain where trust is currency. Whether it’s fraud detection, credit scoring, or robo-advisors, customers won’t adopt opaque “black box” models unless they understand why the system’s decisions are fair.

  • Define the term clearly: XAI (Explainable Artificial Intelligence) refers to techniques and methods that make AI decisions transparent, interpretable, and justifiable.

  • Case study angle:

    • Credit scoring with explainable features (income stability, spending patterns) instead of only hidden neural nets.

    • Fraud detection systems that show “reason codes” for flagged transactions.

    • Robo-advisors that can justify portfolio recommendations in plain language.

  • Critical thinking questions:

    • How does a bank balance the tension between explainability (customer trust) and model accuracy (business efficiency)?

    • What unintended consequences might occur if explanations are too simple or misleading?

Article 2: Healthcare AI: The Role of Explainability in Diagnostics

Key Idea: In healthcare, an AI recommendation can affect life-or-death decisions. Explainability is not optional—it is ethically required.

  • Define key term: Diagnostics refers to the process of identifying disease or conditions from patient data. AI can assist but must show its reasoning path.

  • Case study angle:

    • Medical imaging AI (detecting cancer in X-rays or MRIs) with heat maps that highlight suspicious areas.

    • Clinical decision support systems that provide a ranked list of possible diagnoses with supporting evidence.

    • FDA-approved AI tools that must demonstrate transparency to regulators.

  • Critical thinking questions:

    • Should patients have the right to demand an explanation from AI systems used in their treatment?

    • How might “explainability” differ between a radiologist, a nurse, and the patient themselves?

Article 3: Explainability in Self-Driving Cars: Lessons from Tesla and Waymo

Key Idea: Autonomous vehicles don’t just move people—they carry responsibility. When an accident occurs, the first question is always: Why did the AI act that way?

  • Define context: Self-driving AI integrates perception (sensors, cameras, LIDAR), prediction (other vehicles’ movements), and planning (deciding when to accelerate, brake, or turn).

  • Case study angle:

    • Tesla’s reliance on vision-only systems vs. Waymo’s multi-sensor approach.

    • NTSB (National Transportation Safety Board) investigations into crashes where explainability gaps slowed accountability.

    • “Black box” driving logs vs. interpretable “decision trees” for accident reconstruction.

  • Critical thinking questions:

    • Should regulators mandate real-time explainability in autonomous cars, or is post-incident explanation enough?

    • What happens when explanations reveal biases in training data (e.g., cars misdetecting jaywalkers at night)?

Article 4: XAI in Security and Surveillance: A Double-Edged Sword

Key Idea: In surveillance, explainability ensures accountability—but it can also expose system weaknesses that bad actors might exploit.

  • Define context: Surveillance AI includes facial recognition, anomaly detection in public spaces, and predictive policing.

  • Case study angle:

    • Law enforcement using explainable face-matching scores to justify arrests.

    • Airports applying anomaly detection with transparent “reason codes.”

    • Controversies around predictive policing—how explainability reveals embedded biases against certain communities.

  • Critical thinking questions:

    • Does explainability empower citizens (greater transparency) or empower authorities (better justification of invasive actions)?

    • How do we balance fairness and security when explanations expose the logic of surveillance systems to potential manipulation?

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