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Quick Question A doctor is walking down the street with a boy. The - - PowerPoint PPT Presentation

Mar 05, 2023 373 likes •843 views

Quick Question A doctor is walking down the street with a boy. The boy is the doctors son, but the doctor is not the boys father. How is that possible? GENDER BIAS IN WORD EMBEDDINGS 1 Simple Answer The doctor is the boys

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GENDER BIAS IN WORD EMBEDDINGS

Quick Question

  • A doctor is walking down the street with a boy.

The boy is the doctor’s son, but the doctor is not the boy’s father. How is that possible?

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GENDER BIAS IN WORD EMBEDDINGS

Simple Answer

  • The doctor is the boy’s mother…

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Gender Bias in Word Embeddings

HILA GONEN AND YOAV GOLDBERG BAR ILAN UNIVERSITY WIDS TLV 5/3/19

ACCEPTED TO NAACL 2019

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GENDER BIAS IN WORD EMBEDDINGS

Outline

  • Background
  • Gender Bias
  • Word embeddings
  • Current debiasing methods
  • Post-processing (Bolukbasi et al.)
  • During training (Zhao et al.)
  • Analyzing debiased embeddings
  • Conclusion

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What do we mean by gender bias?

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What do we mean by gender bias?

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What do we mean by gender bias?

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What do we mean by gender bias?

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GENDER BIAS IN WORD EMBEDDINGS

What do we mean by gender bias?

(Zhao et al.,NAACL, 2018)

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What do we mean by gender bias?

(Hendricks et al., 2018)

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GENDER BIAS IN WORD EMBEDDINGS

Word Embeddings

  • We will focus on gender bias in word embeddings

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Word Embeddings

  • We will focus on gender bias in word embeddings

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What are word embeddings?

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Word Embeddings

  • Each word in the vocabulary is represented by a low

dimensional vector (~ )

  • All words are embedded into the same space
  • Similar words have similar vectors
  • (= their vectors are close to each other in the vector space)
  • Word embeddings are successfully used for various NLP

applications

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Training Word Embeddings

  • Learned from raw data
  • The Distributional Hypothesis:
  • words that occur in the same contexts tend to have similar

meanings (Harris, 1954)

  • “You shall know a word by the company it keeps” (Firth, 1957)

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15 LANGUAGE MODELING FOR CODE SWITCHING 12

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16 LANGUAGE MODELING FOR CODE SWITCHING 12

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Word Embeddings

  • TopK lists:

(Mikolov et al. 2013)

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dog

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Word Embeddings

  • TopK lists:

(Mikolov et al. 2013)

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food

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Word Embeddings

  • TopK lists:

(Mikolov et al. 2013)

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nurse

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Word Embeddings

  • TopK lists:

(Mikolov et al. 2013)

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nurse

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Bias in Word Embeddings

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Bias in Word Embeddings

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  • Caliskan et al. replicate a spectrum of known biases from the literature using

word embeddings

  • Show that text corpora contain several types of biases:
  • morally neutral as toward insects or flowers
  • problematic as toward race or gender
  • veridical, reflecting the distribution of gender with respect to careers or first names
  • Introduce methods for identifying these biases
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Bias in Word Embeddings

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Bias in Word Embeddings

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Concepts 1 Concepts 2 Attributes 1 Attributes 2 Flowers: buttercup, daisy, lily Insects: ant, caterpillar, flea Pleasant: freedom, health, love Unpleasant: abuse, crash, filth European American names: Brad, Brendan African American names: Darnell, Lakisha Pleasant: joy, love, peace Unpleasant: agony, terrible Male attributes: male, man, boy Female attributes: female, woman, girl Math words: math, algebra, geometry Arts Words: poetry, art, dance

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Definition of Gender Bias in Word Embeddings

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(NIPS, 2016)

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Definition of Gender Bias in Word Embeddings

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  • We check how similar a word is to “he” and “she” (cosine similarity)
  • Note that we care about the difference between the two
  • This is the projection on the direction of “he – she”*

* This is the gender direction, can be computed using several pairs together (e.g. man-woman, brother-sister)

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Definition of Gender Bias in Word Embeddings

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Zhao et al.

  • bias(consultant) = -0.0023
  • bias(nurse) = -0.2471
  • bias(captain) = 0.1521
  • bias(table) = -0.0003
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Reduce Bias after Training

  • Bolukbasi et al. suggest to remove bias after training:
  • Define a gender direction
  • Define inherently neutral words (nurse as opposed to mother)
  • Zero the projection of all neutral words on the gender direction:
  • The bias of all neutral words is now zero by definition
  • We will address these embeddings as HARD-DEBIASED

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Projection of on gender direction 𝑥

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Reduce bias during training

(EMNLP, 2018)

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Reduce bias during training

  • Zhao et al. suggest to reduce bias during training:
  • Train word embeddings using GloVe (Pennington et al., 2014)
  • Alter the loss to encourage the gender information to concentrate in the last

coordinate

  • To ignore gender information – simply remove the last coordinate

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Reduce bias during training

  • Details:
  • Use two groups of male/female seed words, and encourage words from

different groups to differ in their last coordinate

  • Encourage the representation of neutral-gender words (excluding the last

coordinate) to be orthogonal to the gender direction

  • We will address these embeddings as GN-GLOVE

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Some Results

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  • Bolukbasi et al.:
  • Bias of all inherently-neutral words is zero by definition
  • Generated analogies are less stereotyped
  • Zhao et al.:
  • Decrease bias in co-reference resolution
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Problem solved?

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Problem solved?

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  • Not so fast…
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Clustering male- and female- biased words

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  • We take the most biased words in the vocabulary according to the
  • riginal bias (500 male-biased and 500 female-biased)
  • We cluster them into two clusters using K-means
  • The clusters align with gender with accuracy of:
  • 92.5% compared to 99.99% (HARD-DEBIASED)
  • 85.6% compared to 100% (GN-GLOVE)
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Clustering male- and female- biased words

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HARD-DEBIASED GN-GLOVE

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Bias-by-neighbors

  • Bias is still manifested by the word being close to socially-marked

feminine words

  • A new mechanism for measuring bias:
  • The percentage of male/female socially-biased words among the k

nearest neighbors of the target word

  • Pearson correlation with bias-by-projection:
  • 0.686 compared to 0.741 (HARD-DEBIASED)
  • 0.736 compared to 0.773 (GN-GLOVE)

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Professions

  • We take a predefined list of professions
  • We show correlation between the bias-by-projection and bias-by-

neighbors, before and after debiasing

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Professions

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HARD-DEBIASED GN-GLOVE

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Association with stereotyped words

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  • We evaluate the association between female/male names and

female/male stereotyped words (experiments taken from Caliskan et al.)

  • All the associations have very small p-values

Female-associated Male-associated names Amy, Joan, Lisa John, Paul, Mike family vs. carrer home, parents, children executive, management, professional arts vs. math poetry, art, dance math, algebra, geometry arts vs. science dance, literature, novel science, technology, physics

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Classifying to gender

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  • Can a classifier learn to generalize from some gendered words to
  • thers based only on their representations?

5000 most biased words 1000 4000

test train SVM

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Classifying to gender

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  • Results:
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Conclusion

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  • Word embeddings exhibit gender bias
  • Debiasing is hard!
  • Social gender bias is picked up from the data by the models
  • A lot of the bias information is still recoverable

(even when the bias is low/zero according to the definition usually used)

  • The way we define the bias is important, and needs to be

reconsidered when trying to solve the problem

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Questions?

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Thank you!

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