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Related Questions
- Can you elaborate on how data drift can cause bias in language models? Is data drift related to concept drift or just distributional shifts?
- How do you differentiate between concept drift and data drift, and which one can be a more significant factor in exacerbating bias?
- Can data drift affect a language model's accuracy on a particular dataset or application, or only its ability to learn novel tasks or concepts?
- Can you give a practical example of data drift that affected a real-world language model, such as how Facebook's face-tagging system was initially inclusive of various ethnicities until new demographics were included that showed increased bias?
- Does data drift apply equally across all domains (text classification, speech-to-text, question answering)? Can language models develop data drift due to unseen confounding factors?
- Is it possible for biases that appear in response due to drift to perpetuate new biased patterns rather than reducing previously learned bias levels in initial data distribution through updating learned parameters through exposure?
- Would reinforcement learning of the preprocessed representation rather than data update mitigate and prevent or be even potentially counterproductive from certain unseen confounding interactions if new unseen unseen sub-group trends appear from other domain changes that weren't apparent during development?
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