Algorithm Bias
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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 in natural 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 methods for Word Emb. - WEAT - Test Statistic - Exemple of application - B.E.A.T Summary
- 5. Machine Learning is Everywhere
- 8. Prediction
- 10. What we expect Machine Learning Classifying Clustering Recommending Curating Simulating ?
- 12. Blind application of machine 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
- 14. Google Home (Content Generation)
- 15. Predict Future Criminals (Prediction)
- 16. What we expect Human(bias) and Algorithms
- 17. Google DeepMind
- 18. New York Times
- 19. GDPR’16 MAY 2018
- 20. Right to be Forgotten MAY 2018
- 22. What if… New research opportunities
- 23. F.A.T
- 24. Fairness
- 25. Neuralscience (definitions) . Equity . Fair Compensation For Effort . Social Good . Consequences for Acting Unfairly
- 29. Accountability
- 30. Accountability “The question of whether Machines Can Think... is about as relevant as the question of whether Submarines Can Swim.” ―Edsger Dijkstra
- 31. Accountability
- 32. De quem é a culpa?
- 34. Transparency
- 35. Bias detection techniques NLP
- 37. 1) Word Testing 2) Implicit Association Test
- 39. IAT ( classes lists)
- 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
- 42. Amy IAT (Test 2 sample) John
- 43. IAT idea . Difference between test 3 & 4 indicates implicit gender bias
- 44. 𝚯 1) POS tagging, Lexicon 2) Word Embeddings vector space 3) Test Statistics
- 45. POS / Semantic Parsing / Lexicon VERB She growled, clenching a hammer PRP } NNDT
- 46. 11% 8% 23% … Strong Arrogant Afraid Female {intense, smash, intimidating …} {cocky, smirk, smug, rude …} {shriek, frightened, shiver …} 35% 30% 7% … Male POS / Semantic Parsing / Lexicon
- 47. Word Embedding
- 49. Word England Dp Dp+1 D… Dk 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 D… Dk 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 D… Dk 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) Man is 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 to King, 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 to King, 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 to ‘Woman' (Word2Vec)
- 56. Unbias BOLUKBASI ET AL. (2016) Man is to computer programmer as woman is to homemaker? debiasing word embeddings
- 57. Man is to computer programer, as woman is to ____ Computer_programmer(vec) - Man(vec) + Woman(vec) = Homemaker(vec) Unbias
- 62. WEAT idea =
- 63. Cultural biases are expressed in people’s language CALISKAN ET AL. (2017) Semantics derived automatically from language corpora contain human-like biases
- 65. Effect Size In statistics, an effect size is a quantitative measure of the strength of a phenomenon. (e.g. Pearson’s correlation)
- 67. Cohen's D Interpretation 0.01 very small 0.20 small 0.50 medium 0.80 large 1.20 very large 2.00 huge Cohen’s D interpretation
- 68. Null Hypotesis testing In statistical hypothesis testing, the alternative hypothesis and the null hypothesis are the two rival hypotheses which are compared by a statistical hypothesis test.
- 70. Flowers/Insects IAT Insects Flowers Attribute Pleasant {ant, moth, cockroach, wasp …} {aster, clover, marigold, azalea, violet …} {love, peace, heaven, pleasure, rainbow…} Unpleasant {death, abuse, rotten, filth, ugly …} Class
- 71. Test statistic of null 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 / Cohen’s d CALISKAN ET AL. (2017) Semantics derived automatically from language corpora contain human-like biases Dist1 Dist2
- 75. Flower / Insect
- 77. Hate Speech Hate speech can then be defined as the vilification of a group’s 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 speech communicates extremely negative ideas about a group, or a representative of that group, as defined 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 language’s biases 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 …} Bad {horrible, tragic, sadness, humiliate …} Class
- 81. Sexuality IAT Hetero Homo Attribute Good {straight, heterosexual …} {gay, homosexual …} {lovely, cheerful, friend, smiling …} Bad {horrible, evil, scorn, awful …} Class
- 82. BEAT (bias embedding association test) 1.02 1.73 … Trump Sexuality bias Religion bias Cohen’s D
- 83. Identify offense-given and offense taking
- 84. Compare Languages with BEAT 1.02 1.73 … Trump Sexuality bias Religion bias Cohen’s D 0.35 0.62 … English Cohen’s D
- 85. Explore relations in vector 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 values into online world? .Modern Liberalism .Gender equality .Freedom of religion … Democratic Party’s BEAT Sexuality bias Religion bias
- 87. Algorithm Bias Fairness in natural language processing Raphael Ottoni <raphael@hekima.com.br>, <rapha@dcc.ufmg.br>
- 88. IAT e WEAT usam cohen’d A premissa para que o mapeamento IAT -> WEAT é que o tempo de resposta é equivalente a similaridade de cosseno no embedding É Preciso provar que o WEAT pode ser usado em diferentes contexto, além dos testados no paper origianl
- 89. Algorithm Bias Fairness in natural language processing Raphael Ottoni <raphael@hekima.com.br>, <rapha@dcc.ufmg.br>
- 90. Of Language and Values Characterizing political groups by its ideology biases