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Algorithm Bias
This document discusses algorithm bias and fairness in natural language processing. It introduces the concepts of fairness, accountability, and transparency (F.A.T.) for evaluating bias. Methods for detecting bias in word embeddings like WEAT (Word Embedding Association Test) and statistical tests are explained. The document also provides an example of applying these techniques to detect religious and sexuality biases in word embeddings and explores using them to identify potentially offensive language.
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Algorithm Bias
- 1. Algorithm Bias Fairness innatural language processing Raphael Ottoni <raphael@hekima.com.br>, <rapha@dcc.ufmg.br>
- 2. Summary . Problem Introduction .F.A.T - Fairness - Accountability - Transparency . Background on bias detection
- 3. . Current methodsfor Word Emb. - WEAT - Test Statistic - Exemple of application - B.E.A.T Summary
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- 5. Machine Learning isEverywhere
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- 10. What we expect MachineLearning Classifying Clustering Recommending Curating Simulating ?
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- 12. Blind application ofmachine learning algorithms may amplify biases present in the data BOLUKBASI ET AL. (2016) Man is to computer programmer as woman is to homemaker? debiasing word embeddings âš
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- 14. Google Home (ContentGeneration)
- 15. Predict Future Criminals(Prediction)
- 16. What we expect Human(bias)and Algorithms
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- 20. Right to beForgotten MAY 2018
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- 22. What if⊠New researchopportunities
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- 25. Neuralscience (definitions) . Equity .Fair Compensation For Effort . Social Good . Consequences for Acting Unfairly
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- 30. Accountability âThe question ofwhether Machines Can Think... is about as relevant as the question of whether Submarines Can Swim.â âEdsger Dijkstra
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- 32. De quem Ă©a culpa?
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- 35. Bias detection techniquesâš NLP
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- 37. 1) Word Testing 2)Implicit Association Test
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- 39. IAT ( classeslists)
- 40. .Amy . Joan . Lisa .Sarah . Diana . Kate . Ann . Donna IAT (attributes list) .John . Paul . Mike . Kevin . Steve . Greg . Jeff . Bill Attribute list 1 Attribute list 2
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- 42. Amy IAT (Test 2sample) John
- 43. IAT idea . Differencebetween test 3 & 4 indicates implicit gender bias
- 44. đŻ 1) POS tagging,Lexicon 2) Word Embeddings vector space 3) Test Statistics
- 45. POS / SemanticParsing / Lexicon VERB She growled, clenching a hammer PRP } NNDT
- 46. 11% 8% 23% ⊠Strong Arrogant Afraid Female {intense, smash, intimidatingâŠ} {cocky, smirk, smug, rude âŠ} {shriek, frightened, shiver âŠ} 35% 30% 7% ⊠Male POS / Semantic Parsing / Lexicon
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- 49. Word England Dp Dp+1 DâŠDk York Dog 0.3 -1.3 2.0 2.5 1.5 -0.3 0.1 1.1 1.3 -0.1 0.9 -0.4
- 50. Word England Dp Dp+1 DâŠDk York Dog 0.3 -1.3 2.0 2.5 1.5 -0.3 0.1 1.1 5.3 -0.1 0.9 -0.4
- 51. Word England Dp Dp+1 DâŠDk York Dog 0.3 -1.3 2.0 2.5 1.5 -0.3 0.1 1.1 5.3 -0.1 0.9 -0.4
- 52. Word Embedding (evaluation) Manis to King, as woman is to ____ King(vec) - Man(vec) + Woman(vec) = Queen(vec) Rio is to Brazil, as Paris is to ____ Rio(vec) - Brazil(vec) + Paris(vec) = France(vec)
- 53. Man is toKing, as woman is to ____ King(vec) - Man(vec) + Woman(vec) = Queen(vec) Man is to King, as woman is to ____ King(vec) - Man(vec) + Woman(vec) = Lioness(vec) Word Embedding (evaluation)
- 54. Man is toKing, as woman is to ____ King(vec) - Man(vec) + Woman(vec) = Queen(vec) Man is to doctor, as woman is to ____ doctor(vec) - Man(vec) + Woman(vec) = Nurse(vec) Word Embedding (bias)
- 55. Closest words toâWoman' (Word2Vec)
- 56. Unbias BOLUKBASI ET AL.(2016) Man is to computer programmer as woman is to homemaker? debiasing word embeddings âš
- 57. Man is tocomputer programer, as woman is to ____ Computer_programmer(vec) - Man(vec) + Woman(vec) = Homemaker(vec) Unbias
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- 63. Cultural biases areexpressed in peopleâs language CALISKAN ET AL. (2017) Semantics derived automatically from language corpora contain human-like biases
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- 65. Effect Size In statistics, an effectsize is a quantitative measure of the strength of a phenomenon. (e.g. Pearsonâs correlation)
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- 67. Cohen's D Interpretation 0.01very small 0.20 small 0.50 medium 0.80 large 1.20 very large 2.00 huge Cohenâs D interpretation
- 68. Null Hypotesis testing In statisticalhypothesis testing, the alternative hypothesis and the null hypothesis are the two rival hypotheses which are compared by a statistical hypothesis test.
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- 70. Flowers/Insects IAT Insects Flowers Attribute Pleasant {ant, moth,cockroach, wasp âŠ} {aster, clover, marigold, azalea, violet âŠ} {love, peace, heaven, pleasure, rainbowâŠ} Unpleasant {death, abuse, rotten, filth, ugly âŠ} Class
- 71. Test statistic ofnull hypothesis Classes Attr
- 72. CALISKAN ET AL.(2017) Semantics derived automatically from language corpora contain human-like biases Hypotesis testing
- 73. CALISKAN ET AL.(2017) Semantics derived automatically from language corpora contain human-like biases Hypotesis testing XKCD (201?) https://www.xkcd.com/1478/
- 74. CALISKAN ET AL.(2017) Semantics derived automatically from language corpora contain human-like biases P-Value / Cohenâs d CALISKAN ET AL. (2017) Semantics derived automatically from language corpora contain human-like biases Dist1 Dist2
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- 77. Hate Speech Hate speechcan then be deïŹned as the viliïŹcation of a groupâs Identity in order to oppress its members and deny them equal rights. Cherian ET AL. (2016) Hate Spin: The Manufacture of Religious Offense and Its Threat to Democracy
- 78. Group Identity Hate speechcommunicates extremely negative ideas about a group, or a representative of that group, as deïŹned by identity markers such as race, religion and sexual orientation. Cherian ET AL. (2016) Hate Spin: The Manufacture of Religious Offense and Its Threat to Democracy
- 79. See Group languageâsbiases as potentially offense-giving/taking
- 80. Relegion IAT Islam Christianity Attribute Good {Mosque, Koran,Muslim, Islam} {Church, Bible, Christian, Christianity} {lovely, cheerful, friendship, glad, beautiful âŠ} Bad {horrible, tragic, sadness, humiliate âŠ} Class
- 81. Sexuality IAT Hetero Homo Attribute Good {straight, heterosexualâŠ} {gay, homosexual âŠ} {lovely, cheerful, friend, smiling âŠ} Bad {horrible, evil, scorn, awful âŠ} Class
- 82. BEAT (bias embeddingassociation test) 1.02 1.73 ⊠Trump Sexuality bias Religion bias Cohenâs D
- 83. Identify offense-given andoffense taking
- 84. Compare Languages withBEAT 1.02 1.73 ⊠Trump Sexuality bias Religion bias Cohenâs D 0.35 0.62 ⊠English Cohenâs D
- 85. Explore relations invector space Trump is to White Supremacists, as Hillary is to ____ White_Supremacists(vec) - Trump(vec) + Hillary(vec) = ?(vec)
- 86. 0.76 0.1 ⊠Real Word valuesinto online world? .Modern Liberalism .Gender equality .Freedom of religion ⊠Democratic Partyâs BEAT Sexuality bias Religion bias
- 87. Algorithm Bias Fairness innatural language processing Raphael Ottoni <raphael@hekima.com.br>, <rapha@dcc.ufmg.br>
- 88. IAT e WEATusam cohenâd A premissa para que o mapeamento IAT -> WEAT Ă© que o tempo de resposta Ă© equivalente a similaridade de cosseno no embedding Ă Preciso provar que o WEAT pode ser usado em diferentes contexto, alĂ©m dos testados no paper origianl
- 89. Algorithm Bias Fairness innatural language processing Raphael Ottoni <raphael@hekima.com.br>, <rapha@dcc.ufmg.br>
- 90. Of Language andValues Characterizing political groups by its ideology biases