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Related Questions
- How can domain-specific jargon and terminology impact the representation of knowledge in narrow models?
- What are some potential consequences of incorporating biased data into a model's training data, and how can it affect the model's output?
- Can you explain the concept of 'data provenance' and its relation to the formation of biased models in prompt engineering?
- How can the choice of evaluation metrics influence the development of narrow models, and what are some potential biases to watch out for?
- What role does the 'curate and filter' approach play in mitigating the risk of biased models, and what are its limitations?
- In what ways can the use of knowledge graphs and ontologies impact the formation of biased models, and how can they be used to improve model fairness?
- Can you discuss the importance of 'explainability' in prompt engineering and its connection to model bias, and how can it be achieved in practice?
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