Social Bias Frames: Reasoning about Social and Power Implications of Language (2020.acl-main)
Copied to clipboard
| Challenge: | Language has enormous power to project social biases and reinforce stereotypes on people. |
| Approach: | They propose a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and power differentials onto others. |
| Outcome: | The proposed model can model the pragmatic frames in which people project social biases and power differentials onto others. |
Similar Papers
Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020.acl-main)
Copied to clipboard
| Challenge: | 146 papers analyzing "bias" in NLP systems lack normative reasoning, we find . authors propose three recommendations for work analyzing “bias” in Nlp systems . |
| Approach: | They propose three recommendations for analyzing "bias" in NLP systems . they propose to focus on what kinds of system behaviors are harmful, in what ways, to whom, and why . |
| Outcome: | The proposed methods for measuring or mitigating “bias” are poorly matched to their motivations and do not engage critically with literature outside of NLP. |
Does Reasoning Introduce Bias? A Study of Social Bias Evaluation and Mitigation in LLM Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in large language models have enabled automatic generation of chain-of-thought reasoning . however, when reasoning steps reflect social stereotypes, they can reinforce harmful associations and lead to misleading conclusions. |
| Approach: | They propose a method that detects how model predictions change across incremental reasoning steps. |
| Outcome: | The proposed method outperforms a stereotype-free baseline and improves accuracy. |
Social Bias in Multilingual Language Models: A Survey (2025.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained multilingual models exhibit the same social bias as models processing English texts. |
| Approach: | They examine the literature on bias evaluation and mitigation approaches in multilingual and non-English contexts and identify gaps in the field. |
| Outcome: | The proposed models perform well on multilingual language understanding benchmarks and are consistent with the current literature. |
“Was it “stated” or was it “claimed”?: How linguistic bias affects generative language models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Several studies have identified such linguistic classes of words that occur frequently in natural language text and are bias-inducing by virtue of their framing effects. |
| Approach: | They propose to use linguistic cues to induce subtle biases through implied sentiment and presupposed facts to influence the distribution of the generated text. |
| Outcome: | The proposed models are sensitive to these framing effects, but show that they lead to measurable style and topic differences in the generated text, leading to language that is, on average, more polarised and more skewed towards controversial entities and events. |
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 . |
What social attitudes about gender does BERT encode? Leveraging insights from psycholinguistics (2023.acl-long)
Copied to clipboard
| Challenge: | Much research has focused on evaluating whether large language models encode stereotypical/harmful associations. |
| Approach: | They propose to use two datasets from human experiments to examine how word preferences in a large language model reflect social attitudes about gender. |
| Outcome: | The language model BERT takes into account factors that shape human lexical choice of such language, but may not weigh those factors in the same way people do. |
Ask LLMs Directly, “What shapes your bias?”: Measuring Social Bias in Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to evaluate social bias in large language models have limitations . et al., 1995: stereotypes shape social perceptions without objective basis . |
| Approach: | They propose a method to intuitively quantify social perceptions and suggest metrics to evaluate biases within LLMs. |
| Outcome: | The proposed metrics capture the multi-dimensional aspects of social bias, the paper shows . they show that the proposed metrics can be used to evaluate bias in large language models . |
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2020.acl-main)
Copied to clipboard
| Challenge: | a growing number of studies address the effect of bias on predictions, but no unifying framework exists . a general phenomenon of biased predictive models in NLP is not recent, authors say . |
| Approach: | They propose a unifying framework for identifying and reducing bias in natural language processing . they propose to differentiate two consequences of bias and four potential origins of bias . |
| Outcome: | The proposed framework provides an overview of predictive bias in natural language processing . it differentiates two consequences of bias and four potential origins of bias: label bias, selection bias, model overamplification, and semantic bias. |
Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
| Approach: | They propose to use word embedding association test to detect biases in contextual language models to compare them with other methods. |
| Outcome: | The proposed methods are inconsistent and inconclusive for language models with word embeddings. |
Media Bias Detection Across Families of Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Traditional NLP models have shown good performance in classifying media bias, but require careful model design and extensive tuning. |
| Approach: | They ask how well prompting of large language models can recognize media bias. |
| Outcome: | The prompt-based models deliver comparable performance to traditional models with greatly reduced effort and the availability of context substantially improves results. |