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ai-conversation-impact/tasks/07-positive-metrics.md
claude 0543a43816 Initial commit: AI conversation impact methodology and toolkit 
CC0-licensed methodology for estimating the environmental and social
costs of AI conversations (20+ categories), plus a reusable toolkit
for automated impact tracking in Claude Code sessions.
2026-03-16 09:46:49 +00:00

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Task 7: Define positive impact metrics

Plan: measure-positive-impact Status: DONE Deliverable: New section in impact-methodology.md

What to do

  1. Add a "Positive Impact" section to impact-methodology.md defining proxy metrics:

    • Reach: number of people affected by the output.
    • Counterfactual: would the result have been achieved without this conversation? (none / slower / not at all)
    • Durability: expected useful lifetime of the output.
    • Severity: for bug/security fixes, severity of the issue.
    • Reuse: was the output referenced or used again?

  2. For each metric, document:

    • How to estimate it (with examples).
    • Known biases (e.g., tendency to overestimate reach).
    • Confidence level.

  3. Add a "net impact" formula or rubric that combines cost and value estimates into a qualitative assessment (clearly net-positive / probably net-positive / uncertain / probably net-negative / clearly net-negative).

Done when

  • The methodology document covers both sides of the equation.
  • A reader can apply the rubric to their own conversations.