On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations (2203.13928v1)

Abstract: Multiple metrics have been introduced to measure fairness in various natural language processing tasks. These metrics can be roughly categorized into two categories: 1) \emph{extrinsic metrics} for evaluating fairness in downstream applications and 2) \emph{intrinsic metrics} for estimating fairness in upstream contextualized language representation models. In this paper, we conduct an extensive correlation study between intrinsic and extrinsic metrics across bias notions using 19 contextualized LLMs. We find that intrinsic and extrinsic metrics do not necessarily correlate in their original setting, even when correcting for metric misalignments, noise in evaluation datasets, and confounding factors such as experiment configuration for extrinsic metrics. %al

• The paper demonstrates that intrinsic fairness metrics fail to predict extrinsic fairness outcomes in language models.
• The study reveals significant misalignments between dataset configurations and fairness metric design, impacting evaluation reliability.
• Methodologies including correlation and ablation analyses underscore the influence of noise and configurations on fairness assessments.

Analyzing Intrinsic and Extrinsic Fairness Metrics for Contextualized LLMs

The paper "On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations" presents an analysis of various fairness metrics used in NLP, particularly as they relate to LLMs. The authors make a critical distinction between two types of fairness metrics: intrinsic and extrinsic. Intrinsic metrics are concerned with fairness at the model level, evaluating whether the LLM itself exhibits bias. On the other hand, extrinsic metrics focus on fairness in downstream applications, measuring bias in specific application contexts where these models are deployed.

The authors emphasize that despite efforts to employ these metrics to ensure fairness, there is a lack of correlation between intrinsic and extrinsic metrics. This paper argues that even when adjusting for variables such as metric misalignments, noise in evaluation datasets, and configurations for extrinsic metrics, intrinsic metrics do not necessarily predict extrinsic fairness outcomes.

Summary of Methods and Approaches

1. Metric Categorization: The paper begins by categorizing fairness metrics into intrinsic and extrinsic types. Intrinsic metrics include evaluations based on the contextualized language representations themselves, while extrinsic metrics assess the impact of these representations on specific tasks.
2. Correlation Analysis: The authors investigate the correlation between intrinsic and extrinsic metrics using statistical methods. They highlight how dataset choices, noise levels, and model configurations act as confounding factors that may influence metric results.
3. Ablation Study: An ablation paper is conducted to identify factors contributing to the observed discrepancies between intrinsic and extrinsic metrics. This paper serves to control for potential sources of noise and misalignment in the datasets and metrics in use.

Key Findings and Implications

• Lack of Correlation: One of the notable findings is the demonstrated lack of correlation between intrinsic and extrinsic metrics. This observation questions the effectiveness of relying solely on intrinsic metrics to guarantee fairness in applied settings.
• Dataset and Metric Alignment: The paper identifies the need for better alignment between the design of fairness metrics and the datasets used. The misalignment can lead to metric outcomes that do not truly reflect the fairness characteristics of the models.
• Factors Affecting Metrics: The paper reveals that experimental configurations and noise within datasets are significant contributors to the lack of correlation between metric types, suggesting that future research should focus on standardizing these variables to improve metric reliability.

Theoretical and Practical Implications

Theoretically, the findings challenge existing assumptions about the interplay between intrinsic and extrinsic metrics, urging a revisitation of how fairness is conceptualized and quantified in LLMs. Practically, the results underscore the importance of adopting a more holistic approach by considering both intrinsic and extrinsic factors when developing fairness-oriented benchmarks and deploying models in real-world applications.

Future Directions

Future research could investigate the development of unified fairness metrics that encompass both intrinsic and extrinsic aspects, improving predictability across different applications. Furthermore, exploring the role of different noise levels and dataset constructions on fairness evaluations would provide deeper insights, enhancing the robustness of these metrics in dynamic application environments.

This paper contributes significantly to the discourse on fairness in AI, especially in contextualized LLMs, and highlights the necessity for standardized practices and comprehensive evaluation frameworks to ensure ethical AI deployment.