Explainable AI - Why It Matters and How It Works

Imagine applying for a loan to buy your dream home. You have a solid job, good credit, and a steady income, but the bank's AI system denies your application — without explaining why. This happened to a Swedish couple in 2018, leaving them confused and frustrated.

Similarly, Amazon's AI hiring tool, intended to make recruitment fairer, ended up discriminating against women by downgrading resumes that mentioned women's colleges or activities. The project was eventually abandoned after the bias was discovered.

In healthcare, an AI system used by millions of patients in the U.S. was found to favor white patients over Black patients when deciding who needed extra medical care. The AI used healthcare spending as a proxy for health needs, but because Black patients historically had less access to care, they were unfairly flagged as lower risk.

These examples show why understanding AI decisions is essential. When AI systems influence who gets loans, jobs, or medical treatment, people deserve to know how and why those decisions are made.

Governments and societies worldwide are demanding transparency in AI decision-making. The European Union's General Data Protection Regulation (GDPR) includes a "right to explanation", allowing people to ask why automated decisions were made about them. The EU's upcoming AI Act further requires companies to disclose how their AI systems work, especially in high-risk areas.

In the U.S., similar laws are being proposed to ensure companies assess and explain their AI systems to prevent bias and unfair treatment.

Transparency is not just about rules; it's about fairness and trust. When AI decisions are opaque:

• Biases can go unnoticed and uncorrected;
• Mistakes can harm individuals without accountability;
• People lose trust in AI systems;
• Fixing problems becomes difficult.

Think of it this way: if a human loan officer rejected your loan, you could ask them to explain their reasoning. You might not like the answer, but at least you'd understand it. AI systems should work the same way.

What Is Explainability vs. Interpretability?

• Interpretability means understanding how an AI system works internally - like knowing how a car engine functions.

• Explainability means understanding why the AI made a specific decision -like knowing why your car won't start today.

Most AI systems are complex "black boxes" that are hard to interpret fully, but we can still explain individual decisions.

1. SHAP (SHapley Additive exPlanations)

SHAP assigns fair “credit” to each feature influencing a decision, based on game theory. For example, it can show that your income contributed +$50,000 to a loan approval, while a short employment history subtracted $10,000.

• Works with any AI model
• Explains individual and overall decisions
• Mathematically fair and consistent

Limitations: can be slow on large datasets and complex to understand.

2. LIME (Local Interpretable Model-Agnostic Explanations)

LIME explains individual decisions by creating simple, local models around a specific case. It perturbs input data slightly to see how predictions change, then approximates the decision with an easy-to-understand model.

• Works with any AI model
• Fast and intuitive
• Good for quick, local explanations

Limitations: results can vary between runs and only explain local behavior.

3. Attention Visualization

Used mainly in language AI, this method shows which words or parts of a sentence the AI focused on to make a decision. For example, in sentiment analysis, the AI might focus on words like “terrible” or “amazing.”

• Works with attention-based models
• Intuitive visual insights

Limitations: only applies to specific AI types and may not fully explain causality.

4. Saliency Maps

Used in image-based AI, saliency maps highlight the areas of an image that influenced the AI’s decision. For instance, in medical imaging, it can show which parts of an X-ray led to a diagnosis.

• Visual and intuitive
• Useful for medical and image AI

Limitations: can be noisy or inconsistent.

5. Counterfactual Explanations

These show what needs to change for a different decision. For example: “If your income was $15,000 higher, your loan would be approved.”

• Actionable and easy to understand
• Helps users improve outcomes

Limitations: suggested changes may be unrealistic or impractical.

Healthcare

AI helps diagnose diseases and recommend treatments, but doctors need to understand AI reasoning to trust it. Saliency maps can highlight suspicious areas in scans, while SHAP can show which patient data influenced a diagnosis.

A notable case revealed bias in a healthcare AI that underestimated Black patients’ needs because it used healthcare spending as a proxy for health. Explainable AI helped uncover and fix this bias.

Finance

Banks use AI for credit scoring and loan approvals. Explainable AI helps customers understand decisions and ensures compliance with laws requiring fair treatment.

For example, SHAP can show which factors led to a loan approval, while counterfactuals can advise what changes might improve future applications.

Hiring

AI screens resumes and recommends candidates but can inherit biases. Explainable AI helps detect and reduce bias, providing transparency to candidates and HR managers.

Legal System

AI tools assess risks in bail and sentencing decisions. Explainability is essential to protect defendants’ rights and ensure fairness. Explanations help judges and lawyers understand AI recommendations and challenge them if needed.

As AI systems become more embedded in everyday life, explainability will be key to ensuring ethical and responsible use. Beyond individual decisions, explainable AI helps organizations identify systemic biases and improve their models over time. For example, banks can use global explanations to detect unfair disadvantages, while hospitals can audit AI tools to ensure equitable treatment.

Moreover, explainability fosters trust and collaboration between humans and AI, leading to improved outcomes. This is especially important in healthcare and law, where human oversight remains essential.

Finally, explainable AI supports transparency, public acceptance, and regulatory compliance, paving the way for broader adoption of AI technologies that benefit society.

Explainable AI is essential for fairness, trust, and accountability in systems that affect our lives. Whether applying for a loan, seeking medical care, or navigating hiring or legal processes, individuals deserve to understand how AI decisions are made.

By using tools like SHAP, LIME, attention maps, saliency maps, and counterfactual explanations, we can open the black box and make AI decisions clearer, fairer, and more trustworthy.