Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model (2021.acl-long)
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| Challenge: | Stereotypical language expresses widely-held beliefs about different social categories. |
| Approach: | They propose a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. |
| Outcome: | The proposed model compares favourably with survey-based studies in the psychological literature on stereotypes and shows that it is realistic and effective. |
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| Challenge: | Existing research on stereotypical biases ignores literature on them and results in resource wastage. |
| Approach: | They argue that stereotypes are social constructs shaping human perception and behavior that can produce harmful outcomes under specific conditions. |
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| Challenge: | Large Language Models (LLMs) have been observed to encode harmful associations present in the training data. |
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Social-Group-Agnostic Bias Mitigation via the Stereotype Content Model (2023.acl-long)
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Ali Omrani, Alireza Salkhordeh Ziabari, Charles Yu, Preni Golazizian, Brendan Kennedy, Mohammad Atari, Heng Ji, Morteza Dehghani
| Challenge: | Existing methods for mitigating bias require social-group-specific word pairs for each social attribute (e.g., gender) Existing approaches require only one social attribute, rendering them impractical and costly . |
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StereoDetect: Detecting Stereotypes and Anti-stereotypes the Correct Way Using Social Psychological Underpinnings (2025.findings-emnlp)
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| Challenge: | Stereotypes are known to have harmful effects, making their detection critical . current research focuses on detecting and evaluating stereotypical biases . |
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| Challenge: | Social psychology studies how social stereotypes are shared as part of cultural knowledge . |
| Approach: | They study how stereotypes manifest when potential targets are situated in neutral contexts . they collect human judgments on the presence of stereotypes in generated inferences based on annotator positionality . |
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Quantifying Stereotypes in Language (2024.eacl-long)
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| Challenge: | Existing studies define a sentence as stereotypical and anti-stereotypical, but they lack a fine-grained quantification of stereotypes. |
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A Comprehensive Framework to Operationalize Social Stereotypes for Responsible AI Evaluations (2025.emnlp-main)
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| Challenge: | Recent years have seen unprecedented gains in generative AI models' capabilities across modalitieslanguage, image, audio, and video domains across the globe. |
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Intersectional Stereotypes in Large Language Models: Dataset and Analysis (2023.findings-emnlp)
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| Challenge: | Existing studies on intersectional stereotypes focus on broader, individual categories . current studies focus on single-group stereotypes, such as racial bias against African Americans . |
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| Challenge: | Gender stereotypes are pervasive beliefs about individuals based on their gender that shape societal attitudes, behaviours, and even opportunities. |
| Approach: | They propose eleven strategies to automatically counteract gender stereotypes by generating gender-based counter-stereotypes from a questionnaire to male and female participants. |
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Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach (2025.findings-acl)
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| Challenge: | a new study addresses bias and stereotypes in language models by exploring how learning them together improves performance. |
| Approach: | They propose a dataset for bias and stereotype detection that integrates religion, gender, socio-economic status, race, profession, and others. |
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