Exclusion risk for AI
Description
Exclusion refers to the risk that synthetic data generation processes might overlook or fail to consult with marginalized populations. Such exclusion results in synthetic data that does not accurately represent their experiences, needs, or perspectives.
Why is exclusion a concern for foundation models?
Exclusion can lead to unfair model outcomes, erosion of trust, and potential harm to marginalized groups, as the synthetic data used to train foundation models might perpetuate existing biases and inequalities, potentially resulting in discriminatory outcomes, the exacerbation of social and economic disparities, and the erosion of trust in AI systems among marginalized communities.
Parent topic: AI risk atlas
We provide examples covered by the press to help explain many of the foundation models' risks. Many of these events covered by the press are either still evolving or have been resolved, and referencing them can help the reader understand the potential risks and work toward mitigations. Highlighting these examples are for illustrative purposes only.