Difference Between Global and Local Explanations in AI Models

Understanding the difference between global and local explanations in AI models is essential for anyone who wants transparent, trustworthy, and compliant machine‑learning systems. Global explanations give you a bird’s‑eye view of how a model works overall, while local explanations zoom in on why a particular prediction was made. In this guide we’ll unpack both concepts, compare their strengths, walk through practical implementation steps, and show how you can leverage Resumly’s AI tools to make your own models more explainable.

What Are Global Explanations?

Global explanations describe the overall logic of a model across the entire dataset. They answer questions like “What features generally drive the model’s decisions?” or “How does the model behave on average?” By summarizing the model’s behavior, global methods help data scientists, regulators, and business leaders assess whether the model aligns with domain knowledge and ethical standards.

Common Global Techniques

• Feature importance scores – e.g., permutation importance, mean decrease impurity in tree‑based models.
• Partial dependence plots (PDPs) – show how the predicted outcome changes when a single feature varies, holding others constant.
• SHAP summary plots – aggregate SHAP values across many instances to reveal overall impact patterns.
• Rule extraction – converting complex models into a set of human‑readable if‑then rules.

Stat: A 2023 Gartner survey found that 73% of enterprises rank model interpretability as a top‑5 priority for AI projects. Source

What Are Local Explanations?

Local explanations focus on a single prediction. They answer “Why did the model predict this outcome for this specific record?” This granularity is crucial for debugging, building user trust, and meeting regulatory requirements such as the EU’s AI Act.

Popular Local Techniques

• LIME (Local Interpretable Model‑agnostic Explanations) – builds a simple surrogate model around the instance of interest.
• SHAP (SHapley Additive exPlanations) values – compute contribution of each feature for a single prediction.
• Counterfactual explanations – show minimal changes needed to flip the prediction.
• Individual Conditional Expectation (ICE) plots – display how predictions change for a specific instance as a feature varies.

Global vs. Local: When to Use Which?

Mini‑conclusion: The difference between global and local explanations in AI models lies in scope—global gives the big picture, local zooms into a single decision.

Step‑By‑Step Guide to Adding Explainability to Your AI Project

Below is a checklist you can follow whether you’re building a resume‑matching engine, a churn predictor, or any other ML system.

1. Define the business question – What do stakeholders need to understand?
2. Select the model type – Simpler models (logistic regression) are inherently more interpretable; complex models (deep nets) need post‑hoc tools.
3. Choose explanation scope – Global, local, or both.
4. Pick appropriate libraries – scikit‑learn, shap, lime, eli5.
5. Generate global insights
   • Compute feature importance.
   • Plot PDPs for top features.
6. Generate local insights for critical cases
   • Run SHAP for a sample of predictions.
   • Create counterfactuals for any adverse outcomes.
7. Validate with domain experts – Ensure explanations make sense to non‑technical users.
8. Document and communicate – Use visual dashboards, plain‑language summaries, and embed links to relevant resources (e.g., Resumly’s AI Resume Builder for building transparent candidate profiles).
9. Monitor over time – Track drift in feature importance and update explanations regularly.

Checklist Summary

• Business objective clarified
• Model selected & trained
• Global explanation method implemented
• Local explanation method implemented for edge cases
• Expert review completed
• Documentation published

Do’s and Don’ts for Explainable AI

Do

• Use both global and local explanations when possible.
• Keep explanations simple and avoid jargon.
• Align explanations with regulatory requirements (e.g., GDPR’s right to explanation).
• Test explanations on diverse data slices to uncover hidden bias.

Don’t

• Rely on a single metric like “accuracy” to claim trustworthiness.
• Over‑interpret noisy SHAP values without statistical validation.
• Hide explanation limitations from end users.
• Forget to update explanations after model retraining.

Real‑World Case Studies

1. Resume Matching for Hiring Platforms

A tech recruiting platform used a gradient‑boosted tree model to rank candidates. Global SHAP summary plots revealed that years of experience and skill‑match score dominated decisions, while university ranking had minimal impact—helpful for addressing bias concerns. For a candidate who was unexpectedly rejected, a local LIME explanation highlighted that a missing certification caused the low score, prompting the platform to add a “certification gap” feature to the UI. The team integrated these insights into Resumly’s AI Cover Letter generator, automatically suggesting certifications to add.

2. Credit Scoring in FinTech

A fintech startup deployed a deep neural network for credit risk. Global PDPs showed that debt‑to‑income ratio was the strongest driver, but local SHAP values for a denied applicant revealed that a recent address change contributed heavily to the negative outcome. By providing a counterfactual (“increase monthly income by $500”) the company offered actionable advice, improving customer satisfaction scores by 12%.

Integrating Explainability with Resumly’s AI Tools

Resumly isn’t just about building resumes; it also offers a suite of AI utilities that benefit from transparent models.

• Use the ATS Resume Checker to see how applicant tracking systems score a resume. Pair it with local explanations to tell candidates exactly which keywords need improvement.
• Leverage the Job Match engine and surface global feature importance (e.g., “soft skills matter 30% more than years of experience”) to guide job seekers.
• The Career Personality Test can be explained globally to show users which personality traits influence recommended roles.

By embedding explainability directly into these tools, Resumly helps users make data‑driven career decisions with confidence.

Frequently Asked Questions

1. How do I know if I need global or local explanations? If you want to audit overall model fairness or understand feature trends, go global. If you need to justify a single decision to a user (e.g., why a resume was rejected), use local.

2. Are SHAP values both global and local? Yes. Aggregating SHAP values across many instances yields a global view, while individual SHAP vectors provide local insight.

3. Can I use explanations for non‑ML models like rule‑based systems? Rule‑based systems are inherently interpretable, but you can still generate global summaries (e.g., rule frequency) for documentation.

4. How often should I recompute explanations? Whenever you retrain the model or detect data drift. A quarterly schedule works for most production systems.

5. Do explanations increase inference latency? Local methods like LIME can be computationally heavy; consider pre‑computing explanations for high‑traffic cases or using faster approximations.

6. Are there legal risks if explanations are inaccurate? Yes. Misleading explanations can violate regulations like the EU AI Act. Always validate explanations with domain experts.

7. How do I present explanations to non‑technical users? Use visual aids (bar charts, simple language) and focus on actionable takeaways rather than technical details.

8. Can Resumly help me build explainable AI pipelines? Absolutely. Our blog and resources (see the Resumly Blog) provide templates, and our free tools like the Buzzword Detector help you spot jargon that may confuse users.

Conclusion

Grasping the difference between global and local explanations in AI models empowers you to build systems that are not only accurate but also transparent, fair, and user‑friendly. By combining global overviews with pinpoint‑accurate local insights, you can satisfy regulators, win stakeholder trust, and deliver actionable feedback—whether you’re optimizing a resume‑matching algorithm or a credit‑scoring engine. Leverage Resumly’s AI suite to embed explainability directly into your career‑focused products, and stay ahead in the era of responsible AI.