UCLA-NLP @ EMNLP2020

At UCLA-NLP, our mission is to develop fair, accountable, robust natural language processing technology to benefit everyone. We will present papers at EMNLP 2020 on the following topics.

Fairness in NLP
Cross-Lingual Transfer
PolicyQA

Link to our papers in the virtual conference

Fairness in Natural Language Processing

Natural Language Processing (NLP) models are widely used in our daily lives. Despite these methods achieve high performance in various applications, they run the risk of exploiting and reinforcing the societal biases (e.g. gender bias) that are present in the underlying data. At EMNLP, we present our studies on 1) how to detect bias in a local region of instances, 2) how to control bias in language generation.

LOGAN: Local Group Bias Detection by Clustering

Jieyu Zhao and Kai-Wei Chang, in EMNLP (short), 2020.

QA Sessions: Gather-1I: Nov 17, 02:00-04:00 UTC / 18:00-20:00 PST -1d Paper link in the virtual conference

Full Text Code BibTeX Details

Machine learning techniques have been widely used in natural language processing (NLP). However, as revealed by many recent studies, machine learning models often inherit and amplify the societal biases in data. Various metrics have been proposed to quantify biases in model predictions. In particular, several of them evaluate disparity in model performance between protected groups and advantaged groups in the test corpus. However, we argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. In fact, a model with similar aggregated performance between different groups on the entire data may behave differently on instances in a local region. To analyze and detect such local bias, we propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region and allows us to better analyze the biases in model predictions.

@inproceedings{zhao2020logan,
  author = {Zhao, Jieyu and Chang, Kai-Wei},
  title = {LOGAN: Local Group Bias Detection by Clustering},
  booktitle = {EMNLP (short)},
  presentation_id = {https://virtual.2020.emnlp.org/paper_main.2886.html},
  year = {2020}
}

1/2 Existing studies on measuring bias often consider performance gap between cohorts over the entire test set. But, does it show the whole story? In our #EMNLP20 paper “LOGAN: Local Group Bias Detection by Clustering” (https://t.co/wnpL4Ern8l), joint work with @kaiwei_chang,
— Jieyu Zhao (@jieyuzhao11) November 16, 2020

Related Publications

Measuring Fairness of Text Classifiers via Prediction Sensitivity

Satyapriya Krishna, Rahul Gupta, Apurv Verma, Jwala Dhamala, Yada Pruksachatkun, and Kai-Wei Chang, in ACL, 2022.
Full Text Abstract BibTeX Details

With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have been explored in the recent literature, there is lack of consensus on which metrics most accurately reflect the fairness of a system. In this work, we propose a new formulation : ACCUMULATED PREDICTION SENSITIVITY, which measures fairness in machine learning models based on the model’s prediction sensitivity to perturbations in input features. The metric attempts to quantify the extent to which a single prediction depends on a protected attribute, where the protected attribute encodes the membership status of an individual in a protected group. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness. It also correlates well with humans’ perception of fairness. We conduct experiments on two text classification datasets : JIGSAW TOXICITY, and BIAS IN BIOS, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome. We observe that the proposed fairness metric based on prediction sensitivity is statistically significantly more correlated with human annotation than the existing counterfactual fairness metric.

@inproceedings{krishna2022measuring,
  title = {Measuring Fairness of Text Classifiers via Prediction Sensitivity},
  author = {Krishna, Satyapriya and Gupta, Rahul and Verma, Apurv and Dhamala, Jwala and Pruksachatkun, Yada and Chang, Kai-Wei},
  booktitle = {ACL},
  year = {2022}
}

Fairness in Natural Language Processing

Cross-Lingual Transfer

NLP for Social Good