Alienation

Mitigation strategy

1. **Implement Proactive Equitable Design and Data Auditing** Systematically integrate "Inclusion by Design" principles throughout the entire AI lifecycle, prioritizing the collection of diverse and representative training data to ensure uniform quality-of-service performance across all affected subgroups. This necessitates a mandatory, early-stage assessment of potential Quality-of-Service Harms to prevent unequal performance that disproportionately impacts marginalized individuals. 2. **Employ Granular Bias Mitigation and Subgroup Optimization** Move beyond aggregate fairness metrics to actively optimize model performance and predictive accuracy *within* vulnerable subgroups, as failure to do so can lead to continued or exacerbated disparities. This involves rigorous definition and continuous re-evaluation of intersecting demographic and usage subgroups to ensure that mitigation efforts are targeted and do not negatively affect other populations. 3. **Establish Mechanisms for Agency, Transparency, and Feedback** Design system interfaces that preserve human agency by providing transparent explanations for AI decisions and offering opportunities for user control and override of automated outcomes. Post-deployment, maintain safe, continuous feedback channels from diverse community members to monitor for emerging feelings of self-estrangement and adapt the system based on lived experiences of alienation.