== Resources ==

• A good list of resources on fairness in NLP

  • [github]

• Understanding Metric: A paper about "How to evaluate LLMs":

  • [github]
  • [paper]

• List of Interpretability in ML

  • [github]

== Code ==

• Code on Metric: Python package to assess fairness of machine learning models.

  • [code]

• Demo on Bias: Identify bias and measure fairness of your data

  • [code]

== Code & Resources ==

• Good ref on Fairness: List of fairness metrics for datasets and machine learning models (code and explainations) by IBM

  • [Code]
  • [Paper]

• Code on Metric + relevant papers: A python library that implements many metrics: (e.g., Perplexity, BLEU, Rouge, etc.)

  • [code]
  • [jupyter note-book demo]
  • [paper]

• Python library on responsible AI and explaintaion, by Microsoft

  • [code]