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Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

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):

    ViolationsMean amplificationAccuracy
    CRF1490.04224.14
    CRF + RBA1020.02524.01

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

    ViolationsMean amplificationmAP
    CRF380.04045.40
    CRF + RBA160.02145.38

  • 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

Topics: Semantic role labeling

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.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/d17-1323 · arXiv 1707.09457

@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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