---
title: "Men Also Like Shopping — Zhao 2017"
description: "Vision tasks grounded in language often use structured predictors that exploit correlations between labels."
authors: ["Jieyu Zhao", "Tianlu Wang", "Mark Yatskar", "Vicente Ordonez", "Kai-Wei Chang"]
year: 2017
venue: "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D17-1323/"
doi: "10.18653/v1/d17-1323"
arxiv: "1707.09457"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhao-2017-men-also-like-shopping-reducing/"
---
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# Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

**Authors:** [Jieyu Zhao](https://md.research.elamur.ai/free/en/authors/jieyu-zhao/), [Tianlu Wang](https://md.research.elamur.ai/free/en/authors/tianlu-wang/), [Mark Yatskar](https://md.research.elamur.ai/free/en/authors/mark-yatskar/), [Vicente Ordonez](https://md.research.elamur.ai/free/en/authors/vicente-ordonez/), [Kai-Wei Chang](https://md.research.elamur.ai/free/en/authors/kai-wei-chang/) · **Year:** 2017 · **Venue:** Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D17-1323/) · [PDF](https://aclanthology.org/D17-1323.pdf) · [DOI 10.18653/v1/d17-1323](https://doi.org/10.18653/v1/d17-1323) · [arXiv 1707.09457](https://arxiv.org/abs/1707.09457) · [OpenAlex](https://openalex.org/W2962787423)

**Citations:** 815 citations (openalex-snapshot · 2026-10-14) · [215](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Vision tasks grounded in language often use structured predictors that exploit correlations between labels. Zhao et al. show that these models can pick up gender stereotypes from web-sourced training data and then exaggerate them. They examine two settings: visual semantic role labeling (vSRL) on imSitu and multilabel object classification (MLC) on MS-COCO. They define a bias score, the share of an output (a verb or object) co-occurring with a given gender. Amplification is measured as how far a model's predicted bias exceeds the training bias. Both datasets are strongly skewed, and CRF-based models widen the skew further.

To counter this, the authors propose RBA (Reducing Bias Amplification). This inference-time calibration adds corpus-level constraints that keep predicted gender ratios within a margin of the training statistics. The joint problem is solved with Lagrangian relaxation, which splits it into per-instance subproblems that the original decoder can handle. On the test sets, bias amplification drops by 40.5% for vSRL and 47.5% for MLC, with almost no accuracy cost. This matters because it offers a general, model-agnostic way to stop structured models from magnifying social bias.

## Key points
- **Dataset skew:** more than 45% of imSitu verbs and 37% of MS-COCO objects (80 classes) favor one gender by over 2:1. In the imSitu training data, *man* is the agent in 33% of cooking images, but a trained CRF predicts *man* in only 16%. With RBA, this rises to 20%.
- **Amplification measure:** the bias score for an output–gender pair is its count with that gender divided by its count across all genders. Mean amplification averages the predicted-minus-training bias over pairs that were already biased in training.
- **vSRL test results** (top-1 semantic role accuracy):

  | | Violations | Mean amplification | Accuracy |
  |---|---|---|---|
  | CRF | 149 | 0.042 | 24.14 |
  | CRF + RBA | 102 | 0.025 | 24.01 |

- **MLC test results** (top-1 mean average precision):

  | | Violations | Mean amplification | mAP |
  |---|---|---|---|
  | CRF | 38 | 0.040 | 45.40 |
  | CRF + RBA | 16 | 0.021 | 45.38 |

- **Method details:** RBA uses a 0.05 margin and works as a meta-algorithm over any structured predictor. It is weakest where the initial training bias is low. Code and data are released at github.com/uclanlp/reducingbias.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/)

## Cited by

Cited by

215 papers of our library cite it (of the 140884 whose bibliographies we read); 37 of them have a page here, 178 not yet.

- [Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus](https://md.research.elamur.ai/free/en/papers/bentivogli-2020-gender-danger-evaluating-speech-translation/)
- [Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/)
- [On the Opportunities and Risks of Foundation Models](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
- [Identifying and Reducing Gender Bias in Word-Level Language Models](https://md.research.elamur.ai/free/en/papers/bordia-2019-identifying-reducing-gender-bias-word/)
- [Toward Gender-Inclusive Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cao-2020-gender-inclusive-coreference-resolution/)
- [Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*](https://md.research.elamur.ai/free/en/papers/cao-2021-gender-inclusive-coreference-resolution-analysis/)
- [Toxicity in chatgpt: Analyzing persona-assigned language models](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/)
- [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://md.research.elamur.ai/free/en/papers/dev-2021-harms-gender-exclusivity-challenges-non/)
- [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/)
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- [Restoring Hebrew Diacritics Without a Dictionary](https://md.research.elamur.ai/free/en/papers/findings-2022-gershuni-restoring-hebrew-diacritics-without-dictionary/)
- [Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/)
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- [Towards Understanding and Mitigating Social Biases in Language Models](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/)
- [Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://md.research.elamur.ai/free/en/papers/manzini-2019-black-is-criminal-as-caucasian/)
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- [A Survey on Bias and Fairness in Machine Learning](https://md.research.elamur.ai/free/en/papers/mehrabi-2019-survey-bias-fairness-machine-learning/)
- [HONEST: Measuring Hurtful Sentence Completion in Language Models](https://md.research.elamur.ai/free/en/papers/nozza-2021-honest-measuring-hurtful-sentence-completion/)
- [Gender Bias in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/)
- [Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem](https://md.research.elamur.ai/free/en/papers/saunders-2020-reducing-gender-bias-neural-machine/)
- [Gender Bias in Machine Translation](https://md.research.elamur.ai/free/en/papers/savoldi-2021-gender-bias-machine-translation/)
- [Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
- [Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://md.research.elamur.ai/free/en/papers/shah-2019-predictive-biases-natural-language-processing/)
- [Evaluating Gender Bias in Machine Translation](https://md.research.elamur.ai/free/en/papers/stanovsky-2019-evaluating-gender-bias-machine-translation/)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://md.research.elamur.ai/free/en/papers/sun-2019-mitigating-gender-bias-natural-language/)
- [The Moral Foundations Reddit Corpus](https://md.research.elamur.ai/free/en/papers/trager-2026-moral-foundations-reddit-corpus/)
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- [Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns](https://md.research.elamur.ai/free/en/papers/webster-2018-mind-gap-balanced-corpus-gendered/)
- [Measuring and Reducing Gendered Correlations in Pre-trained Models](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/)
- [Ethical and social risks of harm from Language Models](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
- [A Chinese Challenge Set to Assess Gender Bias in Automated Translation](https://md.research.elamur.ai/free/en/papers/xu-2026-chinese-challenge-set-assess-gender/)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://md.research.elamur.ai/free/en/papers/zhao-2018-gender-bias-coreference-resolution-evaluation/)
- [Learning Gender-Neutral Word Embeddings](https://md.research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/)
- [Gender Bias in Contextualized Word Embeddings](https://md.research.elamur.ai/free/en/papers/zhao-2019-gender-bias-contextualized-word-embeddings/)
- [Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology](https://md.research.elamur.ai/free/en/papers/zmigrod-2019-counterfactual-data-augmentation-mitigating-gender/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhao2017men,
  title = {{Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints}},
  author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  year = {2017},
  doi = {10.18653/v1/d17-1323},
  eprint = {1707.09457},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/d17-1323}
}
```

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