In a case where a Natural Language Processing model starts producing offensive or biased outputs, what steps would you consider taking to rectify the issue without compromising the performance of the model?
- Fine-tuning the model.
- Implementing post-processing filters.
- Re-training with more diverse data.
- Reducing model complexity.
When a model exhibits offensive or biased outputs, re-training with more diverse and representative data is a crucial step to reduce bias and improve performance without compromising model complexity.
Loading...
Related Quiz
- What does interoperability in AI refer to?
- In the context of e-commerce, how is AI commonly utilized to enhance customer experience?
- In the context of transfer learning, what is a potential risk when adapting a pre-trained model to a new task?
- How would you address the challenges of integrating autonomous vehicles into urban areas with complex and dynamic traffic conditions?
- Which of the following ethical considerations deals with the transparency of AI decision-making?