Bias and discrimination (value lock and outcome homogenization)
Because models are not necessarily retrained to reflect evolving societal views, language models risk “value lock- ins,” which “reifies older, less inclusive understandings.”370 Therefore, the continued use of outdated models may limit the presentation or exploration of alternative perspectives. Moreover, the deployment of identical foundation models by various downstream deployers poses a risk of “outcome homogenization,” creating a potential for homogeneity of bias across broad swathes of society. Identical and widely deployed models with prejudicial training datasets could further entrench existing biases in society. This phenomenon, in turn, has the potential to “institutionalize systemic exclusion and reinforce existing social hierarchies.”
ENTITY
1 - Human
INTENT
2 - Unintentional
TIMING
3 - Other
Risk ID
mit738
Domain lineage
1. Discrimination & Toxicity
1.1 > Unfair discrimination and misrepresentation
Mitigation strategy
1. **Systemic Bias Mitigation via Algorithmic Fairness Techniques:** Implement comprehensive algorithmic fairness strategies across the AI system lifecycle, including pre-processing (adjusting training datasets), in-processing (applying factors and constraints during model training), and post-processing (interventions on model outputs). This practice is crucial for identifying, measuring, and reducing harmful biases, directly countering the institutionalization of systemic exclusion inherent in outcome homogenization. 2. **Establish Dynamic Model Retraining and Continuous Ethical Monitoring:** Institute a protocol for dynamic or periodic model retraining, coupled with continuous post-deployment monitoring. This ensures the system's underlying knowledge and ethical alignment are regularly updated to reflect evolving societal values and to prevent "value lock-ins," which reify older, less inclusive understandings. 3. **Promote Interoperability and System-Agnostic Development:** Develop and utilize system-agnostic configurations and interoperability standards for AI components and ecosystems. This strategy is essential for reducing reliance on a single foundational model (algorithmic monoculture) by lowering switching costs and enabling diverse deployments, thereby mitigating the risk of widespread outcome homogenization across society.