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Does Gender Matter? Towards Fairness in Dialogue Systems
Haochen Liu, Jamell Dacon, Wenqi Fan, Hui Liu, Zitao Liu, Jiliang Tang
TL;DR
Dialogue systems can inherit fairness problems from human conversational data, but their fairness has not been well investigated. The paper constructs a benchmark and quantitative measures, evaluates gender and racial bias, and proposes debiasing methods that significantly reduce the bias.
Problem
Fairness in dialogue systems has not been well investigated, despite their use of human conversational data and deployment in society.
Method
The paper constructs a benchmark dataset, formally defines dialogue fairness, introduces quantitative measurements, and proposes two debiasing methods.
Results
The results show significant gender- and race-specific biases in dialogue systems, while the proposed methods effectively mitigate them.
Takeaways & Limitations
Fairness in dialogue systems can be evaluated quantitatively across gender and race and addressed with debiasing methods.
Takeaways & Limitations
The study represents races using standard English for white people and African American English for black people.
Abstract
from arXiv · showhide
Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-world applications such as computer vision and recommendations. For example, recognition algorithms in computer vision are unfair to black people such as poorly detecting their faces and inappropriately identifying them as "gorillas". As one crucial application of AI, dialogue systems have been extensively applied in our society. They are usually built with real human conversational data; thus they could inherit some fairness issues which are held in the real world. However, the fairness of dialogue systems has not been well investigated. In this paper, we perform a pioneering study about the fairness issues in dialogue systems. In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models. Our studies demonstrate that popular dialogue models show significant prejudice towards different genders and races. Besides, to mitigate the bias in dialogue systems, we propose two simple but effective debiasing methods. Experiments show that our methods can reduce the bias in dialogue systems significantly. The dataset and the implementation are released to foster fairness research in dialogue systems.
1 Introduction
Dialogue systems are widely used AI applications, yet their fairness has received limited study. This paper investigates gender and racial bias by building a benchmark, defining quantitative measures, and proposing debiasing methods.
- Dialogue systems support information seeking, task completion, and open-domain entertainment, and are widely deployed in daily life.
- Because dialogue models learn from human conversational data, they may inherit gender and racial biases present in society.
- The paper studies fairness in generative and retrieval dialogue models through three questions: whether bias exists, how to measure it, and how to mitigate it.
- The authors construct a benchmark dataset for studying gender and racial biases in dialogue models.
- They formally define dialogue fairness and introduce measurements for quantitative evaluation.
- They propose two debiasing methods that experiments show can significantly mitigate biases in dialogue systems.
2 Fairness Analysis in Dialogue Systems
The paper defines fairness by comparing dialogue-model measurements across paired groups and tests disparities using parallel contexts and statistical hypothesis testing. It constructs parallel-context data and evaluates fairness through diversity, politeness, sentiment, and attribute-word measurements.
- Fairness definition: Fairness compares a dialogue model’s measurement scores for two groups represented by parallel contexts.Group pairs include male–female and white–black contexts, with group-specific words replaced by counterparts to form mirrored contexts.
- Hypothesis testing: The analysis estimates group-specific expected measurement scores and tests whether they are equal.A parallel context corpus provides samples from the two context distributions, which are used to formulate the fairness hypothesis test.
- Hypothesis testing: A dialogue model is considered unfair when the hypothesis of equal group scores is rejected at p < 0.05.The procedure uses a Z-statistic that approximately follows the standard normal distribution when the sample size is sufficiently large.
- Parallel Context Data Construction: Parallel-context datasets are built by pairing gender words or phrases and standard-English/African American English counterparts, then replacing matched terms in dialogue-corpus contexts.The paper notes that “white people” denotes users of standard English and “black people” users of African American English in this study.
- Fairness measurements: Fairness is evaluated through diversity, politeness, sentiment, and attribute words.Attribute-word fairness compares probabilities of career- and family-related words across gender groups, while diversity, politeness, and sentiment use distinct, offensive-language, and sentiment measures.
3 Experiment on Fairness Test
The experiment evaluates fairness in generative and retrieval dialogue models using gender- and race-parallel contexts. Both models exhibit statistically significant disparities in diversity, politeness, sentiment, and attribute-word associations across groups.
- Models: The study evaluates Seq2Seq generative and Transformer retrieval models, representing the two main classes of chit-chat dialogue systems.Generative models produce responses word by word, whereas retrieval models select responses from a fixed repository.
- Experimental setting: The models are trained on around 2.5 million single-turn Twitter dialogues and evaluated using a separate Twitter dataset of parallel contexts.The evaluation uses 300,000 parallel context pairs for gender and race comparisons.
- Gender fairness: Both models produce more diverse responses for males than females, while females receive more offensive, negative, and less positive responses.Responses also contain more career words for males and more family-related words for females, reflecting observed gender disparities.
- Gender fairness: Gender disparities are statistically significant in almost all tested cases, with hypothesis-test p-values below 0.05.The reported results reject the null hypothesis of no gender bias in dialogue models.
- Race fairness: For race, both models produce less diverse, more offensive, and more negative responses for black people than for white people.Black people are also associated with fewer positive and more unpleasant attribute words.
- Race fairness: Race-related disparities are statistically significant across all measurements, with p-values far below 0.05.The results support the reported unfairness patterns for diversity, politeness, sentiment, and attribute words.
- Overall finding: Overall, dialogue models trained on real-world conversation data share gender- and race-related unfairness observed in the real world.The authors therefore identify fairness handling as important for widely deployed dialogue systems.
4 Debiasing Methods
The paper proposes two debiasing methods for dialogue systems: counterpart data augmentation and word embedding regularization. Experiments show that both reduce gender and racial biases, although word embedding regularization can reduce response diversity.
- Counterpart Data Augmentation: Counterpart data augmentation adds gender- or race-swapped dialogue pairs to the training data to reduce bias at its source.The augmented dataset contains 4,197,883 single-turn dialogues, compared with about 2,580,433 original dialogues.
- Word Embedding Regularization: Word embedding regularization adds a term that decreases the distance between embeddings of gender or race words and their counterparts during training.The method is designed for settings where the learning algorithm cannot access training data.
- Experiments and Results: Both debiasing models significantly reduce gender and racial biases in most evaluated cases, bringing group differences within a reasonable and statistically nonsignificant range.The experiments train CDA and WER models and evaluate them with fairness tests.
- Experiments and Results: Word embedding regularization performs better than counterpart data augmentation for mitigating biases.The comparison is reported across the fairness tests in Table 7.
- Experiments and Results: Word embedding regularization may make responses to two genders or races more similar, creating a trade-off with generated-response diversity.The paper recommends balancing performance and fairness according to the specific situation.
5 Related Work
Related work has studied fairness across machine learning and NLP tasks, including word embeddings, coreference resolution, language modeling, machine translation, and text or dialogue generation.
- Fairness in Machine Learning: Prior fairness research covers classification, regression, graph embedding, and clustering.These studies address fairness in several machine learning settings.
- Word Embedding: Word-embedding studies show stereotypical associations between men and working roles and between women and traditional gender roles, motivating embedding-debiasing methods.The cited work proposes a two-step method for debiasing word embeddings.
- Coreference Resolution: WinoBias benchmarks gender bias in coreference resolution, while data augmentation and word2vec debiasing techniques are used to reduce it.The benchmark targets gender bias in coreference resolution.
- Language Modeling: Language-modeling research measures gender bias in generated text and training data and adds regularization to reduce gender-subspace projections.This line of work builds on soft debiasing techniques for embeddings.
- Machine Translation: Machine-translation studies report male favoritism in translations of stereotypical occupations, while other work applies debiasing methods to translation.The reported examples include translations involving STEM jobs and gender-neutral languages.
- Text/Dialogue Generation: Text and dialogue-generation studies examine gender bias in dialogue datasets, personas, characters, utterances, and multiple dimensions of NLP models.The cited work includes personalized dialogue generation and classifier-based measurement.
6 Conclusion
The paper defines and measures fairness in dialogue systems, evaluates gender and racial biases in generative and retrieval models, and tests two debiasing methods. The results show significant biases and effective bias reduction by the proposed methods.
- Contributions: The study defines fairness formally and introduces four quantitative measurements: diversity, politeness, sentiment, and attribute words.These measurements are used to evaluate fairness in dialogue systems.
- Evaluation: Experiments analyze gender and racial biases in both generative and retrieval-based dialogue models.The evaluation examines fairness issues across the two model types.
- Findings: The results show significant gender- and race-specific biases in dialogue systems.The conclusion summarizes the observed fairness problems across the evaluated systems.
- Debiasing: The proposed debiasing methods effectively reduce biases and ensure fairness in dialogue systems.This conclusion follows the experiments evaluating the two methods.
A Appendix A. Full Lists of Gender, Race and Attribute Words
The appendix lists six categories of words used in the study to analyze gender, race, and attributes.
- Word Categories: The study uses gender words, race words, and attribute words divided into pleasant/unpleasant and career/family categories.Gender words cover male and female; race words cover white and black.
A.1 Gender Words
The gender-word lexicon pairs gender-specific terms across male and female forms, including family roles, occupations, titles, and pronouns.
- A.1 Gender Words: The lexicon contains paired male and female terms such as father–mother, brother–sister, and king–queen.It also includes possessive and pronoun pairs such as he–she and his–her.
- A.1 Gender Words: Pairs cover family relationships, including nephew–niece, grandfather–grandmother, husband–wife, and uncle–aunt.The list includes singular, plural, and compound family terms.
- A.1 Gender Words: Pairs also cover gendered occupations and social roles, including policeman–policewoman, chairman–chairwoman, actor–actress, and waiter–waitress.The lexicon includes role and title variants such as mr.–mrs., sir–madam, and headmaster–headmistress.
A.2 Race Words
The race-word lexicon pairs Standard US English expressions with African American/Black expressions across greetings, social interaction, emotions, and everyday activities.
- A.2 Race Words: The lexicon maps Standard US English expressions to African American/Black alternatives such as going–goin, house–crib, and friend–homie.It includes lexical and phrase-level variants rather than only single-word substitutions.
- A.2 Race Words: It includes alternative greetings, confirmations, farewells, and conversational phrases such as hello–yo, yes–yup, and goodbye–peace.Several concepts have multiple alternatives, including what’s up and alright.
- A.2 Race Words: The list also covers slang for activities, emotions, evaluation, money, clothing, vehicles, and police, including fight me–square up, good–dope, and car–whip.Examples include cool–lit, jewelry–ice, money–chips, and police–feds.
A.3 Career and Family Words
The career-word lexicon consists of terms related to careers, jobs, businesses, and workplace roles.
- A.3 Career and Family Words: The list includes career and employment terms such as academic, accountant, architect, engineer, lawyer, nurse, and occupation.It also contains business and workplace terms including company, corporation, office, and management.
A.4 Pleasant and Unpleasant Words
The section introduces pleasant and unpleasant word categories, with pleasant words covering positive emotions, relationships, qualities, and favorable situations.
- A.4 Pleasant and Unpleasant Words: Pleasant words include positive descriptors and emotions such as awesome, beautiful, excited, happy, joy, love, peaceful, and wonderful.The list also includes favorable concepts such as freedom, health, safety, truth, vacation, and winner.
- A.4 Pleasant and Unpleasant Words: The section explicitly introduces an unpleasant-word category alongside the pleasant-word list.The supplied passage identifies the category but does not enumerate its terms.