AI Deception and Disinformation

This unit examines how adversaries exploit human and machine vulnerabilities to create and spread deception using AI and low‑tech methods. Students study hacker mindsets, AI hallucinations and bias, AI‑generated disinformation and deepfakes, and the ethical/technical ways bad actors can corrupt AI systems to prepare for applied detection and defense work in subsequent units.

Learning Objectives

• Analyze hacker problem‑solving approaches and deception methodologies by comparing at least three attacker profiles and mapping their tactics to potential AI exploitation vectors
• Evaluate the causes, examples, and consequences of AI hallucinations and algorithmic bias by assessing case studies and proposing at least two validation controls to reduce risk
• Demonstrate detection and classification of AI‑driven disinformation and deepfakes by identifying artifacts or behavioral signals in provided multimedia and text samples with a target accuracy threshold in instructor‑led exercises
• Apply ethical and technical mitigation strategies to design a prioritized response plan that counters both low‑tech and AI‑driven deception, including at least three concrete safeguards and escalation steps for digital forensics