Summary of An Actionable Framework For Assessing Bias and Fairness in Large Language Model Use Cases, by Dylan Bouchard
An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases
by Dylan Bouchard
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a decision framework for practitioners to determine which bias and fairness metrics to use for a specific Large Language Model (LLM) use case. The framework is designed to account for both prompt-specific- and model-specific-risk, and defines various metrics to assess each type of risk at the level of an LLM use case. The paper also includes a companion Python toolkit, LangFair, which offers all evaluation metrics in a streamlined implementation. Experiment results demonstrate substantial variation in bias and fairness across use cases, highlighting the importance of use-case-level assessments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make sure language models are fair by giving people a framework to choose the right way to measure bias and unfairness for different uses. It defines risks and metrics for each type of risk, and even includes a special tool to help people implement it easily. The results show that fairness can vary greatly depending on how the model is used. |
Keywords
» Artificial intelligence » Large language model » Prompt
