There has been a recent wave of work assessing the fairness of machine learning models in general, and more specifically, on natural language processing (NLP) models built using machine learning techniques. 
While much work has highlighted biases embedded in state-of-the-art language models, and more recent efforts have focused on how to debias, research assessing the fairness and performance of biased/debiased models on downstream prediction tasks has been limited. 
Moreover, most prior work has emphasized bias along a single dimension such as gender or race. 
In this work, we benchmark multiple NLP models with regards to their fairness and predictive performance across a variety of NLP tasks. 
In particular, we assess intersectional bias - fairness across multiple demographic dimensions. 
The results show that while current debiasing strategies fare well in terms of the fairness-accuracy trade-off (generally preserving predictive power in debiased models), they are unable to effectively alleviate bias in downstream tasks. 
Furthermore, this bias is often amplified across demographic dimensions. We conclude with implications for future NLP debiasing research.

Benchmarking Intersectional Biases in NLP

Psychometric measures of ability, attitudes, perceptions, and beliefs are crucial for understanding user behavior in various contexts including health, security, e-commerce, and finance. Traditionally, psychometric dimensions have been measured and collected using survey-based methods. Inferring such constructs from user-generated text could allow timely, unobtrusive collection and analysis. In this work we construct a corpus for psychometric natural language processing (NLP) related to important dimensions such as trust, anxiety, numeracy, and literacy, in the health domain. We discuss our multi-step process to align user text with their survey-based response items and provide an overview of the resulting testbed, which encompasses survey-based psychometric measures and accompanying user-generated text from 8,502 respondents. Our testbed also encompasses self-reported demographic information, including race, sex, age, income, and education, allowing for measuring bias and benchmarking fairness of text classification methods. We report preliminary results on use of the text to predict/categorize users’ survey response labels and on the fairness of these models. We also discuss the important implications of our work and resulting testbed for future NLP research on psychometrics and fairness.

Constructing a Psychometric Testbed for Fair Natural Language Processing

Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and where progress is made. Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. Using this model, we analyze the ranking reliability of leaderboards. Afterwards, we show the model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. We conclude with recommendations for future benchmark tasks.

Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?

5A: Ethics, Bias, Fairness 1

technical paper

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Virtual Poster Session I: Resources and Evaluation

poster

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Poster 2J: Resources and Evaluation

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Registration is now open

IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

John Lalor

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Presentations

Benchmarking Intersectional Biases in NLP

Constructing a Psychometric Testbed for Fair Natural Language Processing

Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?

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