Discovering and Mitigating Indirect Bias in Attention-Based Model Explanations (2024.findings-naacl)
Copied to clipboard
| Challenge: | Discrimination is the unfair treatment or prejudice directed towards individuals, groups, or certain ideas or beliefs, intentionally or unintentionally. |
| Approach: | They propose an algorithm to detect and mitigate indirect bias in transformer models by leveraging attention explanations. |
| Outcome: | The proposed algorithm shows that it is more accurate than traditional fairness metrics and that it can be used to mitigate bias in transformer models. |
Similar Papers
When and Why Does Bias Mitigation Work? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Neural models exploit shallow surface features to perform language understanding tasks, rather than learning the deeper language understanding and reasoning skills that practitioners desire. |
| Approach: | They propose to use model debiasing techniques to pressure models away from spurious features and to use them to learn useful representations instead. |
| Outcome: | The proposed methods increase models' reliance on hidden biases instead of learning robust features that help them solve a task. |
Learning to Deceive with Attention-Based Explanations (2020.acl-main)
Copied to clipboard
| Challenge: | Attention mechanisms are ubiquitous components in neural network architectures and are often claimed to confer interpretability. |
| Approach: | They propose a method for training models to produce deceptive attention masks by combining weights assigned to designated impermissible tokens with a weighted sum. |
| Outcome: | The proposed method reduces the weight assigned to designated impermissible tokens while still using them across multiple models and tasks. |
Better Explain Transformers by Illuminating Important Information (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing explanations focus on the input and output of the Transformers, resulting in confusing results. |
| Approach: | They propose to highlight important information and eliminate irrelevant information by a refined information flow on top of the layer-wise relevance propagation method. |
| Outcome: | The proposed method outperforms baseline models on classification and question-answering datasets with over 3% to 33% improvement on explanation metrics. |
On the Interaction of Belief Bias and Explanations (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to evaluate explainability fail to account for belief biases affecting human performance . previous studies have shown that neural models can make confident predictions relying on artifacts . |
| Approach: | They propose to account for belief bias in explainability by using models of varying quality and adversarial examples. |
| Outcome: | The proposed methods show that results change when using models of varying quality and adversarial examples. |
Injecting structural hints: Using language models to study inductive biases in language learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study examines the cognitive inductive biases that make language learning possible. |
| Approach: | They structurally bias transformer language models by pretraining on synthetic data . they then evaluate their inductive biases by fine-tuning on three different languages . |
| Outcome: | The proposed method predisposes transformer models to three types of inductive biases . it also fine-tunes the models on three typologically-distant human languages . |
Bias and Fairness in Natural Language Processing (D19-2)
Copied to clipboard
| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
| Approach: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it presents recent community effort to quantify and mitigat bias in natural language processing models . |
| Outcome: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it aims to quantify and mitigate bias in natural language processing models for a wide spectrum of tasks . |
Controlling Bias Exposure for Fair Interpretable Predictions (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to reduce bias in NLP tasks focus on protecting or isolating information related to a sensitive attribute, but they lack control over how much bias is required to be removed. |
| Approach: | They propose a favorable debiasing method that uses sensitive information ‘fairly’, rather than blindly eliminating it. |
| Outcome: | The proposed method achieves a trade-off between debiasing and task performance along with producing debiased rationales as evidence. |
Trustworthy and Explainable Causal Representation Learning in Transformers (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to interpretable representation learning rely on masks that weight the significance of input features, but the origin of these masks is uncertain. |
| Approach: | They propose a causal framework that directly learns identifiable representations from attention weights rather than relying on importance masks. |
| Outcome: | The proposed framework learns identifiable and explainable representations from attention weights, rather than masks, and guarantees faithfulness on real-world datasets. |
Data-Centric Explainable Debiasing for Improving Fairness in Pre-trained Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing data-centric debiasing strategies mainly leverage explicit bias words for counterfactual data augmentation to balance the training data. |
| Approach: | They propose a method which uses an explainability method to search for implicit bias words to assist in debiasing PLMs. |
| Outcome: | Extensive results show that the proposed method achieves state-of-the-art debiasing performance and strong generalization while maintaining predictive abilities. |
Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation (2024.emnlp-main)
Copied to clipboard
| Challenge: | In this study, we examine three considerations for intrinsic debiasing in neural machine translation models. |
| Approach: | They propose to measure the extrinsic bias of neural machine translation models by embedding them in a neural embeddable space and using different tokens to debias them. |
| Outcome: | The proposed methods over-rely on gender stereotypes and over-represent them in their models. |