Source-linked AI summary
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
Soumya Barikeri, Anne Lauscher, Ivan Vulić, Goran Glavaš
TL;DR
Bias evaluation and mitigation resources for conversational language models are scarce, especially those using real-world dialog and measuring downstream performance. The paper introduces RedditBias and a dialog-oriented framework, then benchmarks DialoGPT with four adapted mitigation methods. DialoGPT shows prominent religious biases, while some methods mitigate them while retaining downstream dialog performance.
Problem
Research on conversational-language-model bias is scarce, often limited to a few bias types and artificial resources, while downstream effects of debiasing are largely unmeasured.
Method
The paper constructs RedditBias from real Reddit conversations across four bias dimensions and couples perplexity-based bias measurement with dialog-task evaluation.
Results
DialoGPT exhibits prominent religious biases, while benchmarked debiasing methods mostly mitigate them and retain performance on dialog-oriented downstream tasks.
Takeaways & Limitations
RedditBias provides a real-world benchmark for evaluating bias mitigation alongside conversational model performance.
Takeaways & Limitations
The study does not address intersectional stereotyping arising from interactions among identity characteristics.
Abstract
from arXiv · showhide
Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even bias amplification. Recent work has predominantly focused on measuring and mitigating bias in pretrained language models. Surprisingly, the landscape of bias measurements and mitigation resources and methods for conversational language models is still very scarce: it is limited to only a few types of bias, artificially constructed resources, and completely ignores the impact that debiasing methods may have on the final performance in dialog tasks, e.g., conversational response generation. In this work, we present RedditBias, the first conversational data set grounded in the actual human conversations from Reddit, allowing for bias measurement and mitigation across four important bias dimensions: gender, race, religion, and queerness. Further, we develop an evaluation framework which simultaneously 1) measures bias on the developed RedditBias resource, and 2) evaluates model capability in dialog tasks after model debiasing. We use the evaluation framework to benchmark the widely used conversational DialoGPT model along with the adaptations of four debiasing methods. Our results indicate that DialoGPT is biased with respect to religious groups and that some debiasing techniques can remove this bias while preserving downstream task performance.
1 Introduction
Bias research for conversational systems remains limited despite the ethical risks of encoding societal stereotypes in systems that directly interact with people. RedditBias addresses this gap with a real-world, multidimensional resource and a dialog-oriented evaluation framework.
- Societal biases in language models can create representational harms that perpetuate inequality, making them especially concerning in conversational AI.
- Conversational bias research is scarce, often focuses on gender, uses artificial data, and neglects debiasing effects on dialog performance.
- REDDITBIAS is built from real-world Reddit conversations and manually annotated across religion, race, gender, and queerness.
- The evaluation framework combines a perplexity-based bias measure with downstream dialog performance tests to assess debiasing trade-offs.
- Four literature-based mitigation methods are adapted to conversational language models and evaluated with the proposed framework.
2 Data Set Creation
REDDITBIAS was created by specifying multiple societal bias dimensions, retrieving Reddit comments, and manually annotating comments and target phrases for stereotypical bias. The resulting resource supports training and evaluating mitigation methods across five bias types.
- 2.1 Bias Specifications: The resource covers five bias types across religion, race, gender, and queerness, using target and attribute term sets to formalize each bias.
- 2.1 Bias Specifications: Researchers manually compile near-synonymous target terms and derive negative and positive attribute lists from sociological literature and antonyms.
- 2.2 Candidate Retrieval: Reddit candidates are retrieved by querying combinations of minoritized-group terms and stereotypical attributes, then cleaned and restricted to comments shorter than 150 characters.
- 2.3 Bias Annotation: Annotators assign binary bias labels to comments and corresponding phrases, refining guidelines through calibration before labeling the candidate set.
- 2.3 Bias Annotation: Inter-annotator agreement was .65 Krippendorff’s α for comments and .67 for phrases, with no significant differences across bias types.
3 Evaluation Framework
The evaluation framework measures language-model bias by comparing perplexities of stereotypical phrases with counterfactual counterparts, while also testing intrinsic and downstream dialog performance. This design evaluates whether debiasing reduces bias without harming dialog capabilities.
- The framework couples REDDITBIAS bias evaluation with task-specific performance measures to capture possible downstream effects of debiasing.
- 3.1 Language Model Bias (LMB): LMB compares model perplexity for biased phrases with counterfactual phrases formed by replacing the minoritized target with a dominant-group target.
- 3.1 Language Model Bias (LMB): Outlier phrase pairs are removed before a paired perplexity comparison, and the bias effect is reported as a Student’s two-tailed-test t-value.
- 3.1 Language Model Bias (LMB): A negative t-value indicates negative stereotypical bias, which is statistically significant when p-value significance meets α = 0.05.
- Debiased models are evaluated on Reddit perplexity, dialog state tracking on MultiWoZ, and conversational response generation on DSTC-7.
4 Bias Mitigation Methods
The paper adapts four bias-mitigation approaches for conversational language models, combining additional debiasing losses with counterfactual data augmentation. These methods are evaluated with DialoGPT, which is pretrained on Reddit data.
- DialoGPT is evaluated because it is a widely used conversational language model pretrained on Reddit data.
- Language Model Debiasing Loss: Language Model Debiasing adds a loss penalizing unequal probabilities for paired terms and combines it with causal language-model loss.The λ_LM-to-λ_D ratio regulates the trade-off between language-model capability and bias mitigation.
- Attribute Distance Debiasing: Attribute Distance Debiasing equalizes target-term distances from stereotypical attribute terms using output-layer vector representations and cosine similarity.The final loss is a weighted sum of the causal language-model and attribute-distance debiasing losses.
- Hard Debiasing Loss: Hard debiasing identifies a bias subspace with paired target-term vectors and SVD, then neutralizes attribute representations relative to that subspace.The debiasing loss encourages stereotypical attributes to be orthogonal to the k most prominent bias directions.
- Counterfactual Data Augmentation: Counterfactual Data Augmentation duplicates stereotypical training instances while replacing minoritized-group terms with corresponding dominant-group terms.
5 Experiments and Results
The experiments benchmark DialoGPT and four adapted debiasing methods across bias effects, perplexity, and downstream dialog performance. Religion shows significant stereotypical bias, while HD and CDA reduce it without diminishing conversational capabilities.
- Stereotypical Bias: DialoGPT exhibits significant stereotypical bias for both religion specifications, despite offensive-language removal from its pretraining data.Gender effects are stereotypical but insignificant; Race and Queerness show insignificant effects opposite to the stereotypical direction.
- Stereotypical Bias: HD and CDA significantly remove stereotypical bias for both religion specifications, whereas LMD and ADD do so for only one specification each.LMD succeeds for Religion #2 and ADD for Religion #1, while both attenuate the other specification’s bias effect.
- Stereotypical Bias: All four debiasing methods tend to produce antistereotypical effects for Race, Gender, and Queerness, sometimes significantly associating negative attributes with dominant groups.Examples include criminal with caucasian, nurse with father, and sinful with heterosexual.
- Downstream Dialog Performance: None of the four debiasing methods appears to diminish DialoGPT’s downstream performance on dialog state tracking or response generation.The evaluation reports F1 scores for DST and Bleu-4 scores for CRG.
- Downstream Dialog Performance: LMD drastically increases perplexity on Reddit utterances without negative consequences for dialog state tracking or conversational response generation.The study evaluates original DialoGPT and debiased variants across five bias types using RedditBias train, development, and test splits.
6 Related Work
Prior NLP bias research largely targets static embeddings or language-generation models, while conversational bias work remains limited in dimensions, resources, and downstream evaluation. The paper situates its contribution against these gaps and notes that existing resources may not assess task-performance effects of debiasing.
- Bias in NLP: Bias resources, measures, and mitigation methods have largely targeted static word-embedding models.Prior work introduced analogy-based demonstrations, WEAT, embedding extensions, and broader evaluation frameworks.
- Bias in Language Generation: Language-generation research includes regard-based resources, template-based gender detection, associative-context datasets, and masked-language-model bias benchmarks.CrowS-Pairs does not measure task-oriented model performance, which may degrade after debiasing.
- Bias in Dialog: Research on bias in dialog systems is scarce and mostly focuses on gender bias without measuring downstream performance of debiased models.Existing studies examine classification, controlled mitigation, dataset bias, agreement with stereotypes, response diversity, and generation quality.
- Bias in Dialog: Existing dialog efforts mainly cover gender and race and do not thoroughly analyze debiasing effects on slot-value extraction, dialog state tracking, or conversational response generation.The cited gap concerns performance in dialog tasks rather than bias measurement alone.
7 Conclusion
REDDITBIAS is a manually annotated, real-world Reddit resource for evaluating and debiasing conversational language models across gender, race, religion, and queerness. DialoGPT shows prominent religious biases, while most tested debiasing methods mitigate them without reducing dialog-task performance.
- 7 Conclusion: REDDITBIAS is the first real-world resource dedicated to multi-dimensional bias analysis in conversational language models.It consists of manually annotated biased Reddit comments spanning gender, race, religion, and queerness.
- 7 Conclusion: The study benchmarks DialoGPT on REDDITBIAS and examines how adapted debiasing methods affect dialog-model performance.
- 7 Conclusion: DialoGPT continues to exhibit prominent religious biases despite dedicated bias-mitigation preprocessing of its pretraining data.
- 7 Conclusion: Most benchmarked debiasing methods mitigate those religious biases while retaining performance on dialog-oriented downstream tasks.The paper gives dialog state tracking as an example of a downstream task.
Further Ethical Considerations
The paper identifies scope and governance considerations around its bias resource and debiasing methods. It uses a binary gender specification, does not address intersectional stereotyping, and notes that debiasing technologies can also increase bias.
- Further Ethical Considerations: The gender bias specification adopts a binary dominant-versus-minoritized framing, while broader gender identities are represented under queerness.The paper acknowledges identities such as nonbinary, gender fluid, and polygender but does not include them in the binary gender specification.
- Further Ethical Considerations: Intersectional stereotyping involving interacting identity characteristics, such as social class and gender, is not addressed.The paper attributes this omission to the topic’s complexity.
- Further Ethical Considerations: Debiasing technologies can be used beyond their intended purpose to increase bias and create biased models.
A Bias Specifications
The bias specifications define target-group pairs and stereotypical versus non-stereotypical attribute lists for religion, race, gender, and queerness. These lists operationalize the dimensions through manually compiled demographic expressions and associated descriptors.
- Religion: Religion includes separate comparisons for Jews versus Christians and Muslims versus Christians, with stereotypical and non-stereotypical attribute lists.The specifications provide target expressions and descriptor inventories for each religious comparison.
- Race: Race compares African or Black groups with American, White, Anglo-American, Caucasian, or light-skin counterparts using paired attribute lists.The listed target pairs include alternative lexical forms for African American and related identities.
- Gender: Gender compares female-group terms with male-group counterparts, including woman/man, mother/father, daughter/son, and wife/husband.Additional pairs cover kinship, social-role, and pronoun variants such as she/he.
- Gender: Gender-associated attributes contrast roles such as nurse and secretary with occupations such as surgeon, executive, engineering, and programming.
- Queerness: Queerness compares gay, lesbian, bisexual, homosexual, transgender, sapphic, pansexual, and queer terms with straight or related dominant-group terms.The specification includes pairs such as transgender/cisgender and queer/heterosexual.
- Queerness: Queerness uses attribute lists containing both stereotypical descriptors and contrasting descriptors such as mentally strong, monogamous, lawful, compassionate, and confident.
C Additional Experimental Results
Additional experiments report results for dialog state tracking and response generation using performance measures beyond the primary evaluation.
- C Additional Experimental Results: The additional experimental results evaluate dialog state tracking and response generation with supplementary performance measures.
C.1 Response Generation
The excerpt presents a results layout organized by model and five evaluation dimensions: Rel1, Rel2, Race, Gender, and SexOri.
- The reported evaluation dimensions are Rel1, Rel2, Race, Gender, and SexOri.
- The excerpt is structured around model-level results across these five dimensions.
- No model names or dimension-specific values are visible in the supplied excerpt.
C.2 Dialog State Tracking
The excerpt reports model results across Rel1, Rel2, Race, Gender, and SexOri, including DialoGPT and four listed adaptations.
- DialoGPT reports .9413 for Rel1, Rel2, Race, Gender, and SexOri.
- The listed adaptations are LMD, ADD, HD, and CDA.
- CDA reports .9427, .9452, .9434, .9436, and .9431 across the five listed dimensions.
- LMD, ADD, and HD show distinct values across the five listed dimensions.