Section 153 · Chapter 18, Embodied and Long-Running AI Systems
Testing Social Issues with AI
AI quality includes social consequences: trust, fairness, dependency, manipulation, labor impact, power, and who gets harmed when the system is wrong.
manipulationsocial issues
What to do
- Test representation and access.
- Test manipulation and dependency.
- Test group-level outcomes.
- Test for role displacement and deskilling where it matters.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for manipulation, social issues needed to reproduce work on Testing Social Issues with AI.
- Report results for manipulation, social issues by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In a real release review, social AI testing should combine bias testing, participatory review, segment-level metrics, harm taxonomies, appeal-path audits, longitudinal monitoring, privacy review, and governance decisions about where AI should not be used.
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Cite this page
Jason Arbon. "Testing Social Issues with AI." Testing AI Knowledge Edition, section 153.
https://jarbon.ai/testing-ai/knowledge/ch153-social-issues.html