How to add a harm score to product prioritization

Conventional product prioritization methods such as RICE, ICE, and impact-effort models emphasize expected value, reach, confidence, and delivery cost, which can leave severe but low-frequency user harms underweighted. Drawing on examples including Australia’s Robodebt scheme and account freezes that can deprive customers of essential funds, the author proposes a harm score for products affecting money, identity, health, safety, or legal status. The score evaluates the worst realistic error across severity, reversibility, user vulnerability, and recoverability, using the highest factor rating rather than an average. Low scores prompt proportionate design changes, medium scores require safeguards such as human review, notice, appeals, or reversal deadlines before launch, and critical scores require separate oversight that can block or redesign a feature. In a RICE example involving automated account freezes, the approach turns a low-reach appeals safeguard into a required dependency rather than allowing it to remain low in the backlog. The proposed process is intended to supplement rather than replace existing frameworks by ensuring that teams explicitly consider who bears the cost of an error and whether they can recover.

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