A probability and severity harms matrix that ranks each | AIGP
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A probability and severity harms matrix that ranks each: Which technique most directly serves that

AIGP Understanding How to Govern AI Development Medium

A probability/severity harms matrix prioritizes risks by combining their likelihood and their potential severity.

The question

While designing an AI system, a team wants a structured way to prioritize identified harms so that the most serious and most likely ones receive attention first. Which technique most directly serves that prioritization?

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  1. A mitigation hierarchy that orders responses from avoiding a risk down through reducing and accepting it.
    Almost right and related, but a mitigation hierarchy orders responses to risks rather than ranking the risks themselves.
  2. A probability and severity harms matrix that ranks each risk by how likely and how damaging it would be.
    A probability/severity matrix prioritizes risks by combining likelihood and impact, matching the goal.
  3. A stakeholder mapping exercise that identifies which parties are affected by or can influence the system.
    Almost right as a design activity, but stakeholder mapping identifies affected parties, not risk priority.
  4. A benchmarking study that compares the model's measured performance against comparable systems on the market.
    Plausible design input, but benchmarking compares performance and does not prioritize harms by likelihood and severity.
The trap
Confusing the mitigation hierarchy (ordering responses) with the harms matrix (ranking risks).

How to remember it

A probability/severity harms matrix prioritizes risks by combining their likelihood and their potential severity.

How many of these would you get right?

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