Cedar Works
Cedar Works Explainable AI Lectures
Cedar Works

Explainable

Artificial Intelligence

AI systems make decisions that affect real people — hiring, lending, diagnosis. Understanding those decisions is not optional. This program examines the methods, tools, and limitations of making machine learning interpretable.

Explainable AI lecture materials and conceptual diagrams at Cedar Works
What this covers

Three areas the program addresses directly

Interpretability is not a single technique — it is a cluster of distinct problems that require different approaches depending on the model type, the audience, and the decision context.

Lectures move from foundational concepts to applied methods, with attention to where each tool works and where it breaks down. No single framework is treated as a universal solution.

Interpretability Foundations
  • Distinction between interpretable models and post-hoc explanation
  • Global vs. local explanations — scope and trade-offs
  • Model complexity and the accuracy-interpretability tension
  • When black-box models are and are not acceptable
Explanation Methods
  • LIME — local surrogate model construction
  • SHAP values — Shapley-based feature attribution
  • Attention mechanisms in transformer architectures
  • Counterfactual explanations and recourse
Deployment Context
  • Regulatory requirements — EU AI Act, GDPR Article 22
  • Communicating explanations to non-technical stakeholders
  • Auditing model behaviour in production
  • Failure modes of explanation tools

What makes a model explanation actually useful?

An explanation that satisfies a regulator may confuse the person it is meant to help. An explanation that feels intuitive may be technically misleading. The gap between these is where most XAI work fails.

Lectures draw on documented cases — loan rejections, medical triage tools, recidivism scoring — where explanation quality had measurable consequences. The goal is to develop judgment about when an explanation is sufficient, not just present.

4
Distinct explanation frameworks covered in depth
12
Sequential lectures with worked examples per module
2015
Year Cedar Works began developing this curriculum

How the program is structured

Sequential delivery — each stage builds directly on the previous one

1

Conceptual Ground

Definitions, problem framing, and the vocabulary used across the field. Establishes shared language before any method is introduced.

Foundation
2

Method Survey

LIME, SHAP, integrated gradients, and surrogate models examined side by side — with explicit attention to their assumptions and limits.

Methods
3

Applied Cases

Real deployment scenarios from healthcare, finance, and public sector. Analysis focuses on what went wrong and what a better approach would look like.

Practice
4

Critical Evaluation

How to assess whether an explanation is faithful, stable, and appropriate for its intended audience — including regulators and end users.

Assessment