Bias Mitigation (Debias)

The following guide is designed to present the more general details on using the package to mitigate (debias) bias in word embedding models. The following sections show:

run HardDebias mitigation method on an embedding model to mitigate gender bias (using the fit-transform interface).
apply the target parameter when executing the transformation.
apply the ignore parameter when executing the transformation.
apply the copy parameter when executing the transformation.
run MulticlassHardDebias mitigation method on an word embedding model to mitigate ethnic bias.

Note

For a list of metrics implemented in WEFE, refer to the Debias of the API reference.

Note

If you want to know more about WEFE’s standardization of debias methods, visit Mitigation Framework in the conceptual guides.

Hard Debias

Hard debias is a method that allows mitigating biases through geometric operations on embeddings. This method is binary because it only allows 2 classes of the same bias criterion, such as male or female.

Note

For a multiclass debias (such as for Latinos, Asians and Whites), it is recommended to visit MulticlassHardDebias class.

The main idea of this method is:

1. Identify a bias subspace through the defining sets. In the case of gender, these could be e.g. [['woman', 'man'], ['she', 'he'], ...]

Neutralize the bias subspace of embeddings that should not be biased.

First, we define a set of words that are correct to be related to the bias criterion: the criterion specific gender words. For example, in the case of gender, gender specific words are: ['he', 'his', 'He', 'her', 'she', 'him', 'him', 'She', 'man', 'women', 'men'...].

We then define that all words outside this set should have no relation to the bias criterion and thus have the possibility of being biased. (e.g. for the case of genthe bias direction, such that neither is closer to the bias direction than the other: ['doctor', 'nurse', ...]). Therefore, this set of words is neutralized with respect to the bias subspace found in the previous step.

The neutralization is carried out under the following operation:

\(u\) : embedding
\(v\): bias direction

First calculate the projection of the embedding on the bias subspace.

\[\text{bias subspace} = \frac{v \cdot (v \cdot u)}{(v \cdot v)}\]

Then subtract the projection from the embedding.

\[u' = u - \text{bias subspace}\]

Equalizate the embeddings with respect to the bias direction.

Given an equalization set (set of word pairs such as ['she', 'he'], ['men', 'women'], ..., but not limited to the definitional set) this step executes, for each pair, an equalization with respect to the bias direction. That is, it takes both embeddings of the pair and distributes them at the same distance from the bias direction, so that neither is closer to the bias direction than the other.

The fit parameters define how the neutralization will be calculated. In Hard Debias, you have to provide the the definitional_pairs, the equalize_pairs (which could be the same of definitional pairs) and optionally, a debias criterion_name (to name the debiased model).

The code shown below shows how to run Hard Debias from gender to the test model provided by wefe (reduced word2vec).

from wefe.utils import load_test_model

model = load_test_model()  # load a reduced version of word2vec
model

<wefe.word_embedding_model.WordEmbeddingModel at 0x7f3b83b3b700>

Load the required word sets.

from wefe.datasets import fetch_debiaswe
from wefe.debias.hard_debias import HardDebias

debiaswe_wordsets = fetch_debiaswe()

definitional_pairs = debiaswe_wordsets["definitional_pairs"]
equalize_pairs = debiaswe_wordsets["equalize_pairs"]
gender_specific = debiaswe_wordsets["gender_specific"]


print(f"definitional_pairs: \n{definitional_pairs}")
print(f"equalize_pairs: \n{equalize_pairs}")
print(f"gender_specific: \n{gender_specific}")
print("-" * 70, "\n")

definitional_pairs:
[['woman', 'man'], ['girl', 'boy'], ['she', 'he'], ['mother', 'father'], ['daughter', 'son'], ['gal', 'guy'], ['female', 'male'], ['her', 'his'], ['herself', 'himself'], ['Mary', 'John']]
equalize_pairs:
[['monastery', 'convent'], ['spokesman', 'spokeswoman'], ['Catholic_priest', 'nun'], ['Dad', 'Mom'], ['Men', 'Women'], ['councilman', 'councilwoman'], ['grandpa', 'grandma'], ['grandsons', 'granddaughters'], ['prostate_cancer', 'ovarian_cancer'], ['testosterone', 'estrogen'], ['uncle', 'aunt'], ['wives', 'husbands'], ['Father', 'Mother'], ['Grandpa', 'Grandma'], ['He', 'She'], ['boy', 'girl'], ['boys', 'girls'], ['brother', 'sister'], ['brothers', 'sisters'], ['businessman', 'businesswoman'], ['chairman', 'chairwoman'], ['colt', 'filly'], ['congressman', 'congresswoman'], ['dad', 'mom'], ['dads', 'moms'], ['dudes', 'gals'], ['ex_girlfriend', 'ex_boyfriend'], ['father', 'mother'], ['fatherhood', 'motherhood'], ['fathers', 'mothers'], ['fella', 'granny'], ['fraternity', 'sorority'], ['gelding', 'mare'], ['gentleman', 'lady'], ['gentlemen', 'ladies'], ['grandfather', 'grandmother'], ['grandson', 'granddaughter'], ['he', 'she'], ['himself', 'herself'], ['his', 'her'], ['king', 'queen'], ['kings', 'queens'], ['male', 'female'], ['males', 'females'], ['man', 'woman'], ['men', 'women'], ['nephew', 'niece'], ['prince', 'princess'], ['schoolboy', 'schoolgirl'], ['son', 'daughter'], ['sons', 'daughters'], ['twin_brother', 'twin_sister']]
gender_specific:
['he', 'his', 'He', 'her', 'she', 'him', 'She', 'man', 'women', 'men', 'His', 'woman', 'spokesman', 'wife', 'himself', 'son', 'mother', 'father', 'chairman', 'daughter', 'husband', 'guy', 'girls', 'girl', 'Her', 'boy', 'King', 'boys', 'brother', 'Chairman', 'spokeswoman', 'female', 'sister', 'Women', 'Man', 'male', 'herself', 'Lions', 'Lady', 'brothers', 'dad', 'actress', 'mom', 'sons', 'girlfriend', 'Kings', 'Men', 'daughters', 'Prince', 'Queen', 'teenager', 'lady', 'Bulls', 'boyfriend', 'sisters', 'Colts', 'mothers', 'Sir', 'king', 'businessman', 'Boys', 'grandmother', 'grandfather', 'deer', 'cousin', 'Woman', 'ladies', 'Girls', 'Father', 'uncle', 'PA', 'Boy', 'Councilman', 'mum', 'Brothers', 'MA', 'males', 'Girl', 'Mom', 'Guy', 'Queens', 'congressman', 'Dad', 'Mother', 'grandson', 'twins', 'bull', 'queen', 'businessmen', 'wives', 'widow', 'nephew', 'bride', 'females', 'aunt', 'Congressman', 'prostate_cancer', 'lesbian', 'chairwoman', 'fathers', 'Son', 'moms', 'Ladies', 'maiden', 'granddaughter', 'younger_brother', 'Princess', 'Guys', 'lads', 'Ma', 'Sons', 'lion', 'Bachelor', 'gentleman', 'fraternity', 'bachelor', 'niece', 'Lion', 'Sister', 'bulls', 'husbands', 'prince', 'colt', 'salesman', 'Bull', 'Sisters', 'hers', 'dude', 'Spokesman', 'beard', 'filly', 'Actress', 'Him', 'princess', 'Brother', 'lesbians', 'councilman', 'actresses', 'Viagra', 'gentlemen', 'stepfather', 'Deer', 'monks', 'Beard', 'Uncle', 'ex_girlfriend', 'lad', 'sperm', 'Daddy', 'testosterone', 'MAN', 'Female', 'nephews', 'maid', 'daddy', 'mare', 'fiance', 'Wife', 'fiancee', 'kings', 'dads', 'waitress', 'Male', 'maternal', 'heroine', 'feminist', 'Mama', 'nieces', 'girlfriends', 'Councilwoman', 'sir', 'stud', 'Mothers', 'mistress', 'lions', 'estranged_wife', 'womb', 'Brotherhood', 'Statesman', 'grandma', 'maternity', 'estrogen', 'ex_boyfriend', 'widows', 'gelding', 'diva', 'teenage_girls', 'nuns', 'Daughter', 'czar', 'ovarian_cancer', 'HE', 'Monk', 'countrymen', 'Grandma', 'teenage_girl', 'penis', 'bloke', 'nun', 'Husband', 'brides', 'housewife', 'spokesmen', 'suitors', 'menopause', 'monastery', 'patriarch', 'Beau', 'motherhood', 'brethren', 'stepmother', 'Dude', 'prostate', 'Moms', 'hostess', 'twin_brother', 'Colt', 'schoolboy', 'eldest', 'brotherhood', 'Godfather', 'fillies', 'stepson', 'congresswoman', 'Chairwoman', 'Daughters', 'uncles', 'witch', 'Mommy', 'monk', 'viagra', 'paternity', 'suitor', 'chick', 'Pa', 'fiancé', 'sorority', 'macho', 'Spokeswoman', 'businesswoman', 'eldest_son', 'gal', 'statesman', 'schoolgirl', 'fathered', 'goddess', 'hubby', 'mares', 'stepdaughter', 'blokes', 'dudes', 'socialite', 'strongman', 'Witch', 'fiancée', 'uterus', 'grandsons', 'Bride', 'studs', 'mama', 'Aunt', 'godfather', 'hens', 'hen', 'mommy', 'Babe', 'estranged_husband', 'Fathers', 'elder_brother', 'boyhood', 'baritone', 'Diva', 'Lesbian', 'grandmothers', 'grandpa', 'boyfriends', 'feminism', 'countryman', 'stallion', 'heiress', 'queens', 'Grandpa', 'witches', 'aunts', 'semen', 'fella', 'granddaughters', 'chap', 'knight', 'widower', 'Maiden', 'salesmen', 'convent', 'KING', 'vagina', 'beau', 'babe', 'HIS', 'beards', 'handyman', 'twin_sister', 'maids', 'gals', 'housewives', 'Gentlemen', 'horsemen', 'Businessman', 'obstetrics', 'fatherhood', 'beauty_queen', 'councilwoman', 'princes', 'matriarch', 'colts', 'manly', 'ma', 'fraternities', 'Spokesmen', 'pa', 'fellas', 'Gentleman', 'councilmen', 'dowry', 'barbershop', 'Monks', 'WOMAN', 'fraternal', 'ballerina', 'manhood', 'Dads', 'heroines', 'granny', 'gynecologist', 'princesses', 'Goddess', 'yo', 'Granny', 'knights', 'eldest_daughter', 'HER', 'underage_girls', 'masculinity', 'Girlfriend', 'bro', 'Grandmother', 'grandfathers', 'crown_prince', 'Restless', 'paternal', 'Queen_Mother', 'Boyfriend', 'womens', 'Males', 'SHE', 'Countess', 'stepchildren', 'Belles', 'bachelors', 'matron', 'momma', 'Legs', 'maidens', 'goddesses', 'landlady', 'sisterhood', 'Grandfather', 'Fraternity', 'Majesty', 'Babes', 'lass', 'maternal_grandmother', 'blondes', "ma'am", 'Womens', 'divorcee', 'Momma', 'fathering', 'Effie', 'Lad', 'womanhood', 'missus', 'Sisterhood', 'granddad', 'Mens', 'papa', 'gf', 'sis', 'Husbands', 'Hen', 'womanizer', 'gynecological', 'stepsister', 'Handsome', 'Prince_Charming', 'BOY', 'stepdad', 'teen_ager', 'GIRL', 'dame', 'Sorority', 'beauty_pageants', 'raspy', 'harem', 'maternal_grandfather', 'Hes', 'deliveryman', 'septuagenarian', 'damsel', 'paternal_grandmother', 'paramour', 'paternal_grandparents', 'Nun', 'DAD', 'mothering', 'shes', "HE_'S", 'Nuns', 'teenage_daughters', 'auntie', 'widowed_mother', 'Girlfriends', 'FATHER', 'virile', 'COUPLE', 'grandmas', 'Hubby', 'nan', 'vixen', 'Joan_Crawford', 'stepdaughters', 'endometrial_cancer', 'stepsons', 'loins', 'Grandson', 'Mitchells', 'erections', 'Matron', 'Fella', 'daddies', 'ter', 'Sweetie', 'Dudes', 'Princesses', 'Lads', 'lioness', 'Mamma', 'virility', 'bros', 'womenfolk', 'Heir', 'BROTHERS', 'manliness', 'patriarchs', 'earl', 'sisterly', 'Whore', 'Gynaecology', 'countess', 'convents', 'Oratory', 'witch_doctor', 'mamas', 'yah', 'aunty', 'aunties', 'Heiress', 'lasses', 'Breasts', 'fairer_sex', 'sorority_sisters', 'WIFE', 'Laurels', 'penile', 'nuh', 'mah', 'toms', 'mam', 'Granddad', 'premenopausal_women', 'Granddaddy', 'nana', 'coeds', 'dames', 'herdsman', 'Mammy', 'Fellas', 'Niece', 'menfolk', 'Grandad', 'bloods', 'Gramps', 'damsels', 'Granddaughter', 'mamma', 'concubine', 'Oros', 'Blarney', 'filial', 'broads', 'Ethel_Kennedy', 'ACTRESS', 'Tit', 'fianc', 'Hunk', 'Night_Shift', 'wifey', 'Lothario', 'Holy_Roman_Emperor', 'horse_breeder', 'grandnephew', 'Lewises', 'Muscular', 'feminist_movement', 'Sanan', 'womenâ_€_™', 'Fiancee', 'dowries', 'Carmelite', 'rah', 'n_roller', 'bay_filly', 'belles', 'Uncles', 'PRINCESS', 'womans', 'Homeboy', 'Blokes', 'Charmer', 'codger', 'Delta_Zeta', 'courtesans', 'grandaughter', 'SISTER', 'Highness', 'grandbabies', 'crone', 'Skip_Away', 'noblewoman', 'bf', 'jane', 'philandering_husband', 'Sisqo', 'mammy', 'daugher', 'director_Skip_Bertman', 'DAUGHTER', 'Royal_Highness', 'mannish', 'spinsters', 'Missus', 'madame', 'Godfathers', 'saleswomen', 'beaus', 'Risha', 'luh', 'sah', 'negligee', 'Womenâ_€_™', 'Hos', 'salesgirl', 'grandmom', 'Grandmas', 'Lawsons', 'countrywomen', 'Booby', 'darlin', 'Sheiks', 'boyz', 'wifes', 'Bayi', 'Il_Duce', 'â_€_œMy', 'fem', 'daugther', 'Potti', 'hussy', 'tch', 'Gelding', 'stemmed_roses', 'Damson', 'puh', 'Tylers', 'neice', 'Mutha', 'GRANDMOTHER', 'youse', 'spurned_lover', 'mae', 'Britt_Ekland', 'clotheshorse', 'Carlita_Kilpatrick', 'Cambest', 'Pretty_Polly', 'banshees', 'male_chauvinist', 'Arliss', 'mommas', 'maidservant', 'Gale_Harold', 'Little_Bo_Peep', 'Cleavers', 'hags', 'blowsy', 'Queen_Elizabeth_I.', 'lassies', 'papas', 'BABE', 'ugly_ducklings', 'Jims', 'hellion', 'Beautician', 'coalminer', 'relaxin', 'El_Mahroug', 'Victoria_Secret_Angel', 'shepherdess', 'Mosco', 'Slacks', 'nanna', 'wifely', 'tomboys', 'LAH', 'hast', 'apo', 'Kaplans', 'milkmaid', 'Robin_Munis', 'John_Barleycorn', 'royal_highness', 'Meanie', 'NAH', 'trollop', 'roh', 'Jewess', 'Sheik_Hamad', 'mumsy', 'Big_Pussy', 'chil_dren', 'Aunt_Bea', 'basso', 'sista', 'girlies', 'nun_Sister', 'chica', 'Bubbas', 'massa', 'Southern_belles', 'Nephews', 'castrations', 'Mister_Ed', 'Grandsons', 'Calaf', 'Malachy_McCourt', 'Shamash', 'hey_hey', 'Harmen', 'sonofabitch', 'Donovans', 'Grannie', 'Kalinka', 'hisself', 'Devean', 'goatherd', 'hinds', 'El_Corredor', 'Kens', 'notorious_womanizer', 'goh', 'Mommas', 'washerwoman', 'Samaira', 'Coo_Coo', 'Governess', 'grandsire', 'PRINCE_WILLIAM', 'gramma', 'him.He', 'Coptic_priest', 'Corbie', 'Kennys', 'thathe', 'Pa_Pa', 'Bristols', 'Hotep', 'snowy_haired', 'El_Prado_Ire', 'Girl_hitmaker', 'Hurleys', 'St._Meinrad', 'sexually_perverted', 'authoress', 'Prudie', 'raven_haired_beauty', 'Bonos', 'domestic_shorthair', 'brothas', 'nymphet', 'Neelma', 'Seita', 'stud_muffin', 'St._Judes', 'yenta', 'bare_shouldered', 'Pinkney_Sr.', 'PRINCE_CHARLES', 'Bisutti', 'sistas', 'Blanche_Devereaux', 'Momoa', 'Quiff', 'Scotswoman', 'balaclava_clad_men', 'Louis_Leakey', 'dearie', 'vacuum_cleaner_salesman', 'grandads', 'postulant', 'SARAH_JESSICA_PARKER', 'AUNT', 'Prince_Dauntless', 'Dalys', 'Darkie', 'Czar_Nicholas', 'Lion_Hearted', 'Boy_recliner', 'baby_mamas', 'giantess', 'Lawd', 'GRANNY', 'fianc_e', 'Bilqis', 'WCTU', 'famly', 'Ellas', 'feminazis', 'Pentheus', 'MAMAS', 'Town_Criers', 'Saggy', 'youngman', 'grandam', 'divorcé', 'bosomed', 'roon', 'Simmentals', 'eponymous_heroine', 'LEYLAND', "REE'", "cain't", 'Evelynn', "WAH'", 'sistah', 'Horners', 'Elsie_Poncher', 'Coochie', 'rat_terriers', 'Limousins', 'Buchinski', 'Schicchi', 'Carpitcher', 'Khwezi', "HAH'", 'Shazza', 'Mackeson', "ROH'", 'kuya', 'novice_nun', 'Shei', 'Elmasri', 'ladykiller', '6yo', 'Yenta', 'SHEL', 'pater', 'Souse', 'Tahirah', 'comedian_Rodney_Dangerfield', 'Shottle', 'carryin', 'Sath', "fa'afafine", 'royal_consort', 'hus_band', 'maternal_uncles', 'dressing_provocatively', 'dreamgirl', 'millionaire_industrialist', 'Georgie_Girl', 'Must_Be_Obeyed', 'joh', 'Arabian_stallion', 'ahr', 'mso_para_margin_0in', "SOO'", 'Biddles', 'Chincoteague_Volunteer_Fire', 'Lisa_Miceli', 'gorgeous_brunette', 'fiancŽ', 'Moved_fluently', 'Afternoon_Deelites', 'biker_dude', 'Vito_Spatafore', 'MICK_JAGGER', 'Adesida', 'Reineman', 'witz', 'Djamila', 'Glenroe', 'daddys', 'Romanzi', 'gentlewomen', 'Dandie_Dinmont_terrier', 'Excess_Ire', 'By_SYVJ_Staff', 'zan', 'CONFESSIONS', 'Magees', 'wimmin', 'tash', 'Theatrical_Ire', 'Prince_Charmings', 'chocolate_eclair', 'bron', 'daughers', 'Felly', 'fiftyish', 'Spritely', 'GRANDPA', 'distaffer', 'Norbertines', "DAH'", 'leader_Muammar_Gadaffi', 'swains', 'Prince_Tomohito', 'Honneur', 'Soeur', 'jouster', 'Pharaoh_Amenhotep_III', 'QUEEN_ELIZABETH_II', "Ne'er", 'Galileo_Ire', 'Fools_Crow', 'Lannisters', 'Devines', 'gonzales', 'columnist_Ann_Landers', 'Moseleys', 'hiz', 'busch', 'roastee', 'toyboys', 'Sheffields', 'grandaunt', 'Galvins', 'Giongo', 'geh', 'flame_haired_actress', 'Grammarian', 'Greg_Evigan', 'frontierswoman', 'Debele', 'rabs', 'nymphets', 'aai', 'BREE', 'Shaqs', 'ZAY', 'pappa', 'Housa', 'refrigerator_repairman', 'artificial_inseminations', 'chickie', 'Rippa', 'teenager_Tracy_Turnblad', 'homebred_colt', 'Abigaille', 'hen_pecked_husband', 'businesman', 'her.She', 'Kaikeyi', 'Stittsworth', 'self_proclaimed_redneck', 'Khella', 'NeW', 'Evers_Swindell', 'Asmerom_Gebreselassie', 'Boy_recliners', 'Cliff_Claven', 'Legge_Bourke', 'Costos', "d'_honneur", 'sistahs', 'Cabble', 'sahn', 'CROW_AGENCY_Mont', 'jezebel', 'Harrolds', 'ROSARIO_DAWSON', 'INXS_frontman_Michael_Hutchence', 'Gursikh', 'Dadas', 'VIAGA', 'keen_horsewoman', 'Theodoric', 'Eldery', 'lihn', 'Alice_Kramden', 'Santarina', 'radical_cleric_al_Sadr', 'Curleys', "SY'", 'Fidaa', 'Saptapadi', 'Actor_Sean_Astin', 'Kellita_Smith', 'Doly', 'Libertina', 'Money_McBags', 'Chief_Bearhart', 'choirgirl', 'chestnut_stallion', 'VIGRA', 'BY_JIM_McCONNELL', 'Sal_Vitale', 'Trivia_buffs', 'kumaris', 'fraternal_lodge', 'galpals', 'Borino_Quinn', 'lina', 'LATEST_Rapper', 'Bezar', 'Manro', 'bakla', 'Grisetti', 'blond_bimbo', 'spinster_aunt', 'gurls', 'hiswife', 'paleface', 'Charlye', 'hippie_chicks', 'Khalifas', 'Picture_JUSTIN_SANSON', 'Hepburns', 'yez', 'ALDER', 'Sanussi', 'Lil_Sis', 'McLoughlins', 'Barbra_Jean', 'Lulua', 'thatshe', 'actress_Shohreh_Aghdashloo', 'SIR_ANTHONY_HOPKINS', 'Gloddy', "ZAH'", "ORANGE_'S", 'Danielle_Bimber', 'grandmum', 'Kulkis', 'Brazington', 'Marisa_Lenhard_CFA', 'SIR_JOHN', 'Clareman', 'Aqila', 'Heavily_tattooed', 'Libbys', 'thim', 'elocutionist', 'submissives', 'Inja', 'rahm', 'Agnes_Gooch', 'fake_tits', 'nancy_boys', 'Swaidan', "SHAH'", "ain'ta_bed", 'Shumail_Raj', 'Duchesse', 'diethylstilbestrol_DES', 'colt_foal', 'unfaithful_lover', 'Maseri', 'nevah', 'SAHN', 'Barths', 'Toughkenamon', 'GUEST_STARS', 'him.But', 'Donna_Claspell', 'gingham_dresses', 'Massage_Parlour', 'wae', 'Wasacz', 'Magistra', 'vihl', 'Smriti_Iraani', 'boyish_haircut', 'workingwoman', 'borthers', 'Capuchin_friars', 'Nejma', 'yes_sirs', 'bivocational_pastor', 'Grafters', 'HOPWOOD', 'Nicknamed_Godzilla', 'yos', 'Berkenfield', 'Missis', 'sitcom_Designing_Women', 'Kafoa', 'trainer_Emma_Lavelle', 'sadomasochistic_dungeon', 'iht', 'desperates', 'predessor', 'wolf_cub', 'indigenous_Peruvians', 'Livia_Soprano', 'troh', 'colt_sired', 'BOND_HILL', 'ihl', 'Drydens', 'rahs', 'Piserchia', 'Sonny_Corinthos', 'bankrobber', 'Fwank', 'feisty_redhead', 'booze_guzzling', 'COOPERS', "actress_Q'orianka_Kilcher", 'Cortezar', 'twe', 'Jacoub', 'Cindy_Iannarelli', 'Hell_Raiser', 'Fondly_referred', 'Bridal_Shoppe', 'Noleta', 'Christinas', 'IAGRA', 'LaTanya_Richardson', 'Sang_Bender', 'Assasins', 'sorrel_gelding', 'septugenarian', 'Hissy', 'Muqtada_al_Sadr_mook', 'Pfeni', 'MADRID_AFX_Banco_Santander', 'tuchis', 'LeVaughn', 'Gadzicki', 'transvestite_hooker', 'Fame_jockey_Laffit', 'nun_Sister_Mary', 'SAMSONOV', 'Mayflower_Madam', 'Shaque', 'well.He', 'Trainer_Julio_Canani', 'sorrel_mare', 'minivehicle_joint_venture', 'wife_Dwina', "Aasiya_AH'_see", 'Baratheon', "Rick_O'Shay", 'Mammies', 'goatie', 'Nell_Gwynne', 'charmingly_awkward', 'Slamma', 'DEHL', 'Lorenzo_Borghese', 'ALMA_Wis.', 'Anne_Scurria', 'father_Peruvians_alternately', 'JULIE_ANDREWS', 'Slim_Pickins', 'Victoria_Secret_stunner', "BY'", 'Sanam_Devdas', 'pronounced_luh', 'Pasha_Selim', '中华', 'rson', 'maternal_grandmothers', 'IOWA_CITY_Ia', 'Madame_de_Tourvel', "JAY'", 'Sheika_Mozah_bint_Nasser', 'Hotsy_Totsy', "D'_Ginto", 'singer_Johnny_Paycheck', 'uterine_prolapse_surgery', 'SCOTTDALE_Pa.', 'AdelaideNow_reports', 'Marcus_Schenkenberg', 'Clyse', 'Obiter_Dicta', 'comic_Sam_Kinison', 'bitties', 'ROCKVILLE_Ind.', 'swimsuit_calendars', 'Decicio_Smith', 'Ma_ma', 'Rie_Miyazawa', 'celibate_chastity', 'gwah', "ZAY'", 'HER_Majesty', 'Defrere', 'Las_Madrinas', '簿_聂_翻', 'Bea_Hamill', 'ARCADIA_Calif._Trainer', 'Bold_Badgett', 'stakes_victress', 'Hoppin_Frog', 'Narumiya', 'Flayfil', 'hardman_Vinnie_Jones', 'Marilyn_Monroe_lookalike', 'Kivanc_Tatlitug', 'Persis_Khambatta', 'SINKING_SPRING_Pa.', 'len_3rd', 'DEAR_TRYING', 'Farndon_Cheshire', 'Krishna_Madiga', 'daughter_Princess_Chulabhorn', 'Marshall_Rooster_Cogburn', 'Kitty_Kiernan', 'Yokich', 'Jarou', 'Serdaris', 'ee_ay', 'Montifiore', 'Chuderewicz', 'Samuel_Le_Bihan', 'filly_Proud_Spell', 'Umm_Hiba', 'pronounced_koo', 'Sandy_Fonzo', "KOR'", 'Fielder_Civil_kisses', 'Federalsburg_Maryland', 'Nikah_ceremony', 'Brinke_Stevens', 'Yakama_Tribal_Council', 'Capuchin_Father', 'wife_Callista_Bisek', 'Beau_Dare', 'Bedoni', 'Arjun_Punj', 'JOHNNY_KNOXVILLE', 'cap_tain', 'Alderwood_Boys', 'Chi_Eta_Phi', 'ringleader_Charles_Graner', 'Savoies', 'Lalla_Salma', 'Mrs._Potiphar', 'fahn', 'name_Taylor_Sumers', 'Vernita_Green', 'Bollywood_baddie', 'BENBROOK_Texas', 'Assemblyman_Lou_Papan', 'virgin_brides', 'Cho_Eun', 'CATHY_Freeman', 'Uncle_Saul', 'Lao_Brewery', 'Ibo_tribe', 'ruf', 'rival_Edurne_Pasaban', 'Hei_Shangri_La', 'Mommy_dearest', 'interest_Angola_Sonogal', 'Ger_Monsun', 'PUSSYCAT_DOLL', 'Crown_Jewels_Condoms', 'Lord_Marke', 'Patootie', 'Nora_Bey', 'huntin_shootin', 'Minister_Raymond_Tshibanda', 'La_Nina_la_NEEN', 'signature_Whoppers', 'estranged_hubby_Kevin_Federline', "UR'", 'pill_poppin', "GEHR'", 'purebred_Arabians', 'husbandly_duties', 'VIAGRA_TIMING', 'Hereford_heifer', 'hushed_monotone_voice', 'Pola_Uddin', 'Wee_Jimmy_Krankie', 'Kwakwanso', 'Our_Galvinator', 'shoh', 'Codependency_Anonymous_Group', "LA'", "Taufa'ahau", 'Invincible_Spirit_colt', "SAH'_dur", 'MOUNT_CARMEL_Pa.', 'watches_attentively', 'SNL_spinoffs', 'Seth_Nitschke', 'Duns_Berwickshire', 'defendant_Colleen_LaRose', "Silky_O'Sullivan", 'Highcliff_Farm', "REN'", 'Comestar', 'Satisfied_Frog', 'Jai_Maharashtra', 'ATTICA_Ind.', 'lover_Larry_Birkhead', 'Tami_Megal', 'chauvinist_pigs', 'Phi_sorority', 'Micronesian_immigrant', 'Lia_Boldt', 'Sugar_Tits', 'actress_Kathy_Najimy', 'zhoo', 'Colombo_underboss', 'Katsav_accusers', 'Bess_Houdini', 'rap_mogul_Diddy', 'companions_Khin_Khin', 'Van_Het', 'Mastoi_tribe', 'VITALY', 'ROLLING_STONES_rocker', 'womanizing_cad', 'LILY_COLE', 'paternal_grandfathers', 'Lt._Col._Kurt_Kosmatka', 'Kasseem_Jr.', 'Ji_Ji', 'Wilburforce', 'VIAGRA_DOSE', 'English_Sheepdogs', 'pronounced_Kah', 'Htet_Htet_Oo', 'Brisk_Breeze', 'Eau_du', 'BY_MELANIE_EVANS', 'Neovasc_Medical', 'British_funnyman_RICKY', '4YO_mare', 'Hemaida', 'MONKTON', 'Mrs_Mujuru', 'BaGhana_BaGhana', 'Shaaban_Abdel_Rahim', 'Edward_Jazlowiecki_lawyer', 'Ajman_Stud', 'manly_pharaoh_even', 'Serra_Madeira_Islands', "FRAY'", 'panto_dames', 'Khin_Myo', 'dancer_Karima_El_Mahroug', 'CROWN_Princess', 'Baseball_HOFer', 'Hasta_la_Pasta', 'GIRLS_NEXT_DOOR', 'Benedict_Groeschel', 'Bousamra', 'Ruby_Rubacuori_Ruby', 'Monde_Bleu', 'Un_homme_qui', 'Taylor_Sumers', 'Rapper_EMINEM', 'Joe_Menchetti', "VAY'", 'supermodel_NAOMI_CAMPBELL', 'Supermodel_GISELE_BUNDCHEN', 'Au_Lait', 'Radar_Installed', 'THOMAS_TOWNSHIP_Mich.', 'Rafinesque', 'Herman_Weinrich', 'Abraxas_Antelope', 'raspy_voiced_rocker', 'Manurewa_Cosmopolitan_Club', 'Paraone', 'THE_LEOPARD', 'Boy_Incorporated_LZB', 'Dansili_filly', 'Lumpy_Rutherford', 'unwedded_bliss', 'Bhavna_Sharma', 'Scarvagh', 'en_flagrante', 'Mottu_Maid', 'Dowager_Queen', 'NEEN', 'model_Monika_Zsibrita', 'ROSIE_PEREZ', 'Mattock_Ranger', 'Valorous', 'Surpreme', 'Marwari_businessmen', 'Grandparents_aunts', 'Kimberley_Vlaeminck', 'Lyn_Treece_Boys', 'PDX_Update', 'Virsa_Punjab', 'eyelash_fluttering', 'Pi_fraternity', 'HUNTLEIGH_Mo.', 'novelist_Jilly_Cooper', 'Naha_Shuri_temple', 'Yasmine_Al_Massri', 'Mu_Gamma_Xi', 'Mica_Ertegun', 'Ocleppo', 'VIAGRA_CONTRAINDICATIONS', 'daughter_PEACHES', 'trainer_Geoff_Wragg', 'OVERNIGHT_DELIVERY', 'Fitts_retiree', 'de_Tourvel', 'Lil_Lad', 'north_easterner', 'Aol_Weird_News', 'Somewhat_improbably', 'Sikh_panth', 'Worcester_2m_7f', 'Zainab_Jah', 'OLYMPIC_medalist', 'Enoch_Petrucelly', 'collie_Lassie', "LOW'", 'clumsiness_Holloway', 'ayr', "OHR'", 'ROLLING_STONES_guitarist', "LAH'_nee", 'Ian_Beefy_Botham', 'Awapuni_trainer', 'Glamorous_Granny', 'Chiang_Ching', 'MidAtlantic_Cardiovascular_Associates', 'Yeke', 'Seaforth_Huron_Expositor', 'Westley_Cary_Elwes', 'Cate_Blanchett_Veronica_Guerin', 'Bellas_Gate', 'witch_Glinda', 'wives_mistresses', 'Woodsville_Walmart', '2YO_colt', 'Manav_Sushant_Singh', 'Pupi_Avati_Il', 'Sigma_Beta_Rho', 'Bishop_Christopher_Senyonjo', 'Vodou_priest', 'Rubel_Chowdhury', 'Claddagh_Ring', "TAH'_duh_al", "al_Sadr_mook_TAH'", 'ROBIN_GIBB', "GAHN'", 'BY_THOMAS_RANSON', 'sister_Carine_Jena', 'Lyphard_mare', 'summa_cum', 'Semenya_grandmother_Maputhi', 'Clare_Nuns', 'Talac', 'sex_hormones_androgens', 'majeste', 'Saint_Ballado_mare', 'Carrie_Huchel', 'Mae_Dok', 'wife_Dieula', 'Earnest_Sirls', 'spoof_bar_mitzvah', 'von_Boetticher', 'Audwin_Mosby', 'Case_presentationWe', 'Vincent_Papandrea', "KRAY'", 'Sergi_Benavent', 'Le_Poisson', 'Von_Cramm', 'Patti_Mell', 'Raymi_Coya', 'Benjamin_BeBe_Winans', 'Nana_Akosua', 'Auld_Acquaintance', 'Desire_Burunga', 'Company_Wrangler_Nestea', 'ask_Krisy_Plourde', 'JUANITA_BYNUM', 'livia', 'GAMB', 'Gail_Rosario_Dawson', 'Ramgarhia_Sikh', 'Catholic_nun_Sister', 'FOUR_WEDDINGS_AND', 'Robyn_Scherer', 'brother_King_Athelstan', 'Santo_Loquasto_Fences', 'Wee_Frees', 'MARISOL', 'Soliloquy_Stakes', 'Whatever_Spoetzl', "Marc'Aurelio", 'mon_petit', 'Sabbar_al_Mashhadani', "KAY'_lee", "m_zah_MAH'", 'BY_TAMI_ALTHOFF', 'hobbit_Samwise_Gamgee', 'Bahiya_Hariri_sister', 'daddy_Larry_Birkhead', 'Sow_Tracey_Ullman', 'coach_Viljo_Nousiainen', 'Carmen_Lebbos', 'conjoined_twins_Zainab', 'Rob_Komosa', 'ample_bosomed', 'Ageing_rocker', 'psychic_Oda']
----------------------------------------------------------------------

Instantiate and fit the parameters of the debias transformation. In the fit stage, parameters such as bias direction are calculated and embeddings are prepared for the equalization stage.

hd = HardDebias(verbose=False, criterion_name="gender")

hd.fit(
    model, definitional_pairs=definitional_pairs, equalize_pairs=equalize_pairs,
)

<wefe.debias.hard_debias.HardDebias at 0x7f3b810ad6d0>

Mitigation Parameters

The parameters of the transform method are relatively standard for all methods. The most important ones are target, ignore and copy.

In the following example we use ignore and copy, which are described below:

ignore (by default, None):

A list of strings that indicates that the debias method will perform the debias in all words except those specified in this list. In case it is not specified, debias will be executed on all words. In case ignore is not specified or its value is None, the transformation will be performed on all embeddings. This may cause words that are specific to social groups to lose that component (for example, leaving 'she' and 'he' without a gender component).
copy (by default True):

if the value of copy is True, method attempts to create a copy of the model and run debias on the copy. If False, the method is applied on the original model, causing the vectors to mutate.

Warning

WARNING:** Setting copy with True requires at least 2x RAM of the size of the model. Otherwise the execution of the debias may raise MemoryError.

The following transformation is executed using a copy of the model, ignoring the words contained in gender_specific.

gender_debiased_model = hd.transform(model, ignore=gender_specific, copy=True)

Copy argument is True. Transform will attempt to create a copy of the original model. This may fail due to lack of memory.
Model copy created successfully.

100%|██████████| 13013/13013 [00:00<00:00, 118668.18it/s]

Measuring the Decrease of Bias

Using the metrics and queries shown in the Bias Measurement user guide, we can measure whether there was a change in the measured gender bias between the original model and the debiased model.

from wefe.datasets import load_weat
from wefe.query import Query
from wefe.metrics import WEAT

weat_wordset = load_weat()
weat = WEAT()

Next, we measure the gender bias exposed by query 1 (Male terms and Female terms wrt Career and Family) with respect to the debiased model and the original.

gender_query_1 = Query(
    [weat_wordset["male_terms"], weat_wordset["female_terms"]],
    [weat_wordset["career"], weat_wordset["family"]],
    ["Male terms", "Female terms"],
    ["Career", "Family"],
)
print(gender_query_1, "\n", "-" * 70, "\n")

biased_results_1 = weat.run_query(gender_query_1, model, normalize=True)
debiased_results_1 = weat.run_query(
    gender_query_1, gender_debiased_model, normalize=True
)

print("Debiased vs Biased (absolute values)")
print(
    round(abs(debiased_results_1["weat"]), 3),
    "<",
    round(abs(biased_results_1["weat"]), 3),
)

<Query: Male terms and Female terms wrt Career and Family
- Target sets: [['male', 'man', 'boy', 'brother', 'he', 'him', 'his', 'son'], ['female', 'woman', 'girl', 'sister', 'she', 'her', 'hers', 'daughter']]
- Attribute sets:[['executive', 'management', 'professional', 'corporation', 'salary', 'office', 'business', 'career'], ['home', 'parents', 'children', 'family', 'cousins', 'marriage', 'wedding', 'relatives']]>
 ----------------------------------------------------------------------

Debiased vs Biased (absolute values)
0.047 < 0.463

The above results show that there was a decrease in the measured gender bias.

Next, we measure the gender bias exposed by query 2 (Male Names and Female Names wrt Pleasant and Unpleasant terms) with respect to the debiased model and the original.

gender_query_2 = Query(
    [weat_wordset["male_names"], weat_wordset["female_names"]],
    [weat_wordset["pleasant_5"], weat_wordset["unpleasant_5"]],
    ["Male Names", "Female Names"],
    ["Pleasant", "Unpleasant"],
)

print(gender_query_2, "\n", "-" * 70, "\n")

biased_results_2 = weat.run_query(
    gender_query_2, model, normalize=True, preprocessors=[{}, {"lowercase": True}]
)
debiased_results_2 = weat.run_query(
    gender_query_2,
    gender_debiased_model,
    normalize=True,
    preprocessors=[{}, {"lowercase": True}],
)

print("Debiased vs Biased (absolute values)")
print(
    round(abs(debiased_results_2["weat"]), 3),
    "<",
    round(abs(biased_results_2["weat"]), 3),
)

<Query: Male Names and Female Names wrt Pleasant and Unpleasant
- Target sets: [['John', 'Paul', 'Mike', 'Kevin', 'Steve', 'Greg', 'Jeff', 'Bill'], ['Amy', 'Joan', 'Lisa', 'Sarah', 'Diana', 'Kate', 'Ann', 'Donna']]
- Attribute sets:[['caress', 'freedom', 'health', 'love', 'peace', 'cheer', 'friend', 'heaven', 'loyal', 'pleasure', 'diamond', 'gentle', 'honest', 'lucky', 'rainbow', 'diploma', 'gift', 'honor', 'miracle', 'sunrise', 'family', 'happy', 'laughter', 'paradise', 'vacation'], ['abuse', 'crash', 'filth', 'murder', 'sickness', 'accident', 'death', 'grief', 'poison', 'stink', 'assault', 'disaster', 'hatred', 'pollute', 'tragedy', 'divorce', 'jail', 'poverty', 'ugly', 'cancer', 'kill', 'rotten', 'vomit', 'agony', 'prison']]>
 ----------------------------------------------------------------------

Debiased vs Biased (absolute values)
0.055 < 0.074

Again, the above results show that there was a decrease in the measured gender bias.

Target Parameter

If a set of words is specified in target parameter, the debias method is performed only on the embeddings associated with this set. In the case of providing None, the transformation is performed on all vocabulary words except those specified in ignore. By default None.

In the following example, the target parameter is used to execute the transformation only on the career and family word set:

targets = [
    "executive",
    "management",
    "professional",
    "corporation",
    "salary",
    "office",
    "business",
    "career",
    "home",
    "parents",
    "children",
    "family",
    "cousins",
    "marriage",
    "wedding",
    "relatives",
]

hd = HardDebias(verbose=False, criterion_name="gender").fit(
    model, definitional_pairs=definitional_pairs, equalize_pairs=equalize_pairs,
)

gender_debiased_model = hd.transform(model, target=targets, copy=True)

Copy argument is True. Transform will attempt to create a copy of the original model. This may fail due to lack of memory.
Model copy created successfully.

100%|██████████| 16/16 [00:00<00:00, 9428.05it/s]

Next, a bias test is run on the mitigated embeddings associated with the target words.

In this case, the value of the metric is lower on the query executed on the mitigated model than on the original one. These results indicate that there was a mitigation of bias on embeddings of these words.

gender_query_1 = Query(
    [weat_wordset["male_terms"], weat_wordset["female_terms"]],
    [weat_wordset["career"], weat_wordset["family"]],
    ["Male terms", "Female terms"],
    ["Career", "Family"],
)
print(gender_query_1, "\n", "-" * 70, "\n")

biased_results_1 = weat.run_query(gender_query_1, model, normalize=True)
debiased_results_1 = weat.run_query(
    gender_query_1, gender_debiased_model, normalize=True
)

print("Debiased vs Biased (absolute values)")
print(
    round(abs(debiased_results_1["weat"]), 3),
    "<",
    round(abs(biased_results_1["weat"]), 3),
)

<Query: Male terms and Female terms wrt Career and Family
- Target sets: [['male', 'man', 'boy', 'brother', 'he', 'him', 'his', 'son'], ['female', 'woman', 'girl', 'sister', 'she', 'her', 'hers', 'daughter']]
- Attribute sets:[['executive', 'management', 'professional', 'corporation', 'salary', 'office', 'business', 'career'], ['home', 'parents', 'children', 'family', 'cousins', 'marriage', 'wedding', 'relatives']]>
 ----------------------------------------------------------------------

Debiased vs Biased (absolute values)
0.047 < 0.463

However, if a bias test is run with words that were outside the target word set, the results are almost the same. The slight difference in the metric scores lies in the fact that the equalize sets were still equalized.

Warning

The equalization process can modify embeddings that have not been marked in the target. In Hard Debias, equalization can be deactivated by delivering an empty equalize set ([]).

gender_query_2 = Query(
    [weat_wordset["male_names"], weat_wordset["female_names"]],
    [weat_wordset["pleasant_5"], weat_wordset["unpleasant_5"]],
    ["Male Names", "Female Names"],
    ["Pleasant", "Unpleasant"],
)

print(gender_query_2, "\n", "-" * 70, "\n")

biased_results_2 = weat.run_query(
    gender_query_2, model, normalize=True, preprocessors=[{}, {"lowercase": True}]
)
debiased_results_2 = weat.run_query(
    gender_query_2,
    gender_debiased_model,
    normalize=True,
    preprocessors=[{}, {"lowercase": True}],
)

print("Debiased vs Biased (absolute values)")
print(
    round(abs(debiased_results_2["weat"]), 3),
    ">",
    round(abs(biased_results_2["weat"]), 3),
)

<Query: Male Names and Female Names wrt Pleasant and Unpleasant
- Target sets: [['John', 'Paul', 'Mike', 'Kevin', 'Steve', 'Greg', 'Jeff', 'Bill'], ['Amy', 'Joan', 'Lisa', 'Sarah', 'Diana', 'Kate', 'Ann', 'Donna']]
- Attribute sets:[['caress', 'freedom', 'health', 'love', 'peace', 'cheer', 'friend', 'heaven', 'loyal', 'pleasure', 'diamond', 'gentle', 'honest', 'lucky', 'rainbow', 'diploma', 'gift', 'honor', 'miracle', 'sunrise', 'family', 'happy', 'laughter', 'paradise', 'vacation'], ['abuse', 'crash', 'filth', 'murder', 'sickness', 'accident', 'death', 'grief', 'poison', 'stink', 'assault', 'disaster', 'hatred', 'pollute', 'tragedy', 'divorce', 'jail', 'poverty', 'ugly', 'cancer', 'kill', 'rotten', 'vomit', 'agony', 'prison']]>
 ----------------------------------------------------------------------

Debiased vs Biased (absolute values)
0.08 > 0.074

Note that the equalization caused the bias of the debiased model to be slightly larger than the original.

Saving the Debiased Model

To save the mitigated model one must access the KeyedVectors (the gensim object that contains the embeddings) through wv and then use the save method to store the method in a file.

gender_debiased_model.wv.save("gender_debiased_glove.kv")

Multiclass Hard Debias

Multiclass Hard Debias is a generalized version of Hard Debias that enables multiclass debiasing. Generalized refers to the fact that this method extends Hard Debias in order to support more than two types of social target sets within the definitional set.

For example, for the case of religion bias, it supports a debias using words associated with Christianity, Islam and Judaism.

The usage is very similar to Hard Debias with the difference that the definitional_sets can be larger than pairs.

from wefe.datasets import fetch_debias_multiclass
from wefe.debias.multiclass_hard_debias import MulticlassHardDebias

multiclass_debias_wordsets = fetch_debias_multiclass()
weat_wordsets = load_weat()
weat = WEAT()

ethnicity_definitional_sets = multiclass_debias_wordsets["ethnicity_definitional_sets"]
ethnicity_equalize_sets = list(
    multiclass_debias_wordsets["ethnicity_analogy_templates"].values()
)

print(f"ethnicity_definitional_sets: \n{ethnicity_definitional_sets}")
print(f"ethnicity_equalize_sets: \n{ethnicity_equalize_sets}")
print("-" * 70, "\n")

mhd = MulticlassHardDebias(verbose=False, criterion_name="ethnicity")
mhd.fit(
    model=model,
    definitional_sets=ethnicity_definitional_sets,
    equalize_sets=ethnicity_equalize_sets,
)

ethnicity_debiased_model = mhd.transform(model, copy=True)

ethnicity_definitional_sets:
[['black', 'caucasian', 'asian'], ['african', 'caucasian', 'asian'], ['black', 'white', 'asian'], ['africa', 'america', 'asia'], ['africa', 'america', 'china'], ['africa', 'europe', 'asia']]
ethnicity_equalize_sets:
[['manager', 'executive', 'redneck', 'hillbilly', 'leader', 'farmer'], ['doctor', 'engineer', 'laborer', 'teacher'], ['slave', 'musician', 'runner', 'criminal', 'homeless']]
----------------------------------------------------------------------

copy argument is True. Transform will attempt to create a copy of the original model. This may fail due to lack of memory.
Model copy created successfully.

100%|██████████| 13003/13003 [00:00<00:00, 18357.20it/s]

Measuring the Decrease of Bias

The following code compares the execution of a query measuring ethnic bias in the original model vs. in the debiased model.

ethnicity_query = Query(
    [
        multiclass_debias_wordsets["white_terms"],
        multiclass_debias_wordsets["black_terms"],
    ],
    [
        multiclass_debias_wordsets["white_biased_words"],
        multiclass_debias_wordsets["black_biased_words"],
    ],
    ["european_american_names", "african_american_names"],
    ["white_biased_words", "black_biased_words"],
)

print(ethnicity_query, "\n", "-" * 70, "\n")

biased_results = weat.run_query(
    ethnicity_query, model, normalize=True, preprocessors=[{}, {"lowercase": True}],
)
debiased_results = weat.run_query(
    ethnicity_query,
    ethnicity_debiased_model,
    normalize=True,
    preprocessors=[{}, {"lowercase": True}],
)

print("Debiased vs Biased (absolute values)")
print(
    round(abs(debiased_results_2["weat"]), 3),
    "<",
    round(abs(biased_results_2["weat"]), 3),
)

<Query: european_american_names and african_american_names wrt white_biased_words and black_biased_words
- Target sets: [['america', 'caucasian', 'europe', 'white'], ['africa', 'african', 'black']]
- Attribute sets:[['manager', 'executive', 'redneck', 'hillbilly', 'leader', 'farmer'], ['slave', 'musician', 'runner', 'criminal', 'homeless']]>
 ----------------------------------------------------------------------

Debiased vs Biased (absolute values)
0.08 < 0.074