Harm Anticipation

Harm anticipation is systematically thinking through how an AI product could cause harm — before it does. Preventive design, not reactive crisis management.

The work is unglamorous and easy to skip. Done well, it produces specific testable mitigations. Done badly, it produces a doc nobody reads.

Categories of AI harm

• Direct harm: the AI outputs something harmful — dangerous advice, discriminatory content, privacy violations
• Facilitated harm: the AI helps a user do something harmful, even if the AI's output itself is benign
• Emergent harm: harmful patterns from scale or interaction effects, not from any single output
• Omission harm: the AI fails to act when it should — not flagging a crisis, not escalating
• Erosion harm: gradual negative effects — dependency, deskilling, manipulation, trust erosion

Structured anticipation

Work through each harm category with five lenses: