Chapter 9: Understanding AI Behavior

Opening the Black Box

Modern AI can diagnose diseases, approve loans, or recommend jobs—yet often functions as a “black box,” showing inputs and outputs without revealing how decisions are made. This opacity raises problems: we need to know why a system erred, where bias arises, and how to ensure fairness in high-stakes domains like healthcare or justice.

AI interpretability addresses these issues through methods that expose neural network behavior, generate human-readable explanations, and enable audits. Responsible AI also requires fairness across groups, accountability for decisions, and governance that balances innovation with safety.

These are not just technical challenges but societal ones, shaping how humans and AI interact, how algorithms are regulated, and how trust is built in powerful systems.

Subchapters

📘 The Black Box Problem

Understand why modern AI systems are difficult to interpret and why this opacity creates challenges for trust and deployment.

📗 Interpretability Methods and Techniques

Learn about the tools and techniques researchers use to peer inside AI systems and understand their decision-making processes.

📕 Bias and Fairness in AI Systems

Explore how bias enters AI systems, different definitions of fairness, and approaches to building more equitable algorithms.

📙 Accountability and Algorithmic Auditing

Examine frameworks for AI accountability, governance structures, and systematic approaches to auditing algorithmic systems.

📒 The Environmental Cost of AI

Examine the energy consumption, carbon footprint, and resource usage of training and running AI systems.