Editing Responsible AI and AI Safety (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert evaluation of responsible AI systems requires a multi-stakeholder approach:

'''Technical audits''': Independent evaluation of model performance across demographic subgroups, adversarial robustness testing, and privacy vulnerability assessment (membership inference attacks, model inversion).

'''Process audits''': Review of data collection, labeling, and model development processes for compliance with responsible AI practices. Who labeled the training data? What were their demographics? What quality controls existed?

'''Impact assessments''': Before deployment, systematic analysis of potential harms. Who could be negatively affected? Are there populations particularly vulnerable to errors? What is the cost of false positives vs. false negatives?

'''Ongoing monitoring''': Post-deployment, measure real-world performance disparities, user complaint patterns, and feedback loop effects. Model performance in production often degrades or develops new biases as the population of users changes.

Expert practitioners treat responsible AI as a '''continuous process''', not a one-time pre-deployment checklist. Regular red teaming, community engagement, and transparent reporting are hallmarks of mature responsible AI practice.
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