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iCAST 2020 Invited Talk: Fairness in AI Service and Awareness for Computational Social Science

iCAST 2020 Invited Talk: Fairness in AI Service and Awareness for Computational Social Science

iCAST2020にて基調講演を行った際のスライドになります。

http://www.icast2020.org.cn/

Abstract
This talk aims to present an overview of the common pitfalls for applying machine learning techniques to real-world problems from a perspective of fairness. This talk mainly highlights the importance of diversity of the data and the problem related to algorithmic bias. In the age of information overload, machine learning becomes increasingly important in everyday life. There has been a growing interest in discovering the harmful effect of bias in machine learning and a way to take fairness into service. Based on our research and experience in the industry, we discuss open questions for further application.

Biography
Tomoki Fukuma is the founder and CEO of TDAI Lab, a machine learning AI startup, founded in Tokyo in 2016. He is now a Ph.D. student in the Department of Systems Innovation, School of Engineering, The University of Tokyo. The main area of his interest is learning the true quality of the content in an online platform. This includes topics in unbiased learning-to-rank, recommendation, and AI for Society. He is also a Japanese ballroom dancer, representing Japan from 2016 to 2019.

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株式会社TDAI Lab

December 16, 2020
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  1. ɹConfidentialɹ© TDAI Lab All rights reserved. 1 Fairness in AI

    Service and Awareness for Computational Social Science Tomoki Fukuma TDAI Lab Co., Ltd. CEO The 11th International Conference on Awareness Science and Technology (iCAST 2020) December 7-9, 2020
  2. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 2 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • How to Fight Against Bias • Summary
  3. ɹConfidentialɹ© TDAI Lab All rights reserved. Information Overload With the

    widespread use of the Internet, the opinions of various people can easily be collected in large quantities. https://www.statista.com/chart/19058/how-many-websites-are-there/
  4. ɹConfidentialɹ© TDAI Lab All rights reserved. Information Overload https://reputationstacker.com/how-many-reviews-do-you-need/ Users

    cannot find or don’t know what they want. Service providers have to systematically select valuable information for users. ɹɹ But How ?
  5. ɹConfidentialɹ© TDAI Lab All rights reserved. ɹɹ Information Collection from

    Pull to Push Pull Push “search, recommendation” Users are moving from traditional pull-type information collection to push-type information collection
  6. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 6 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • How to Fight Against Bias • Summary
  7. ɹConfidentialɹ© TDAI Lab All rights reserved. Technique Summary Voting Mechanism

    Unsupervised Learning Supervised learning (review, Q&A, SNS) ɾGraph-Based Ranking ɾAnomaly Detection ɾCollaborative Filtering ɾLearning-to-Rank ɾHelpfulness Prediction ɾFake Detection Wisdom of the Crowd Machine Learning
  8. ɹConfidentialɹ© TDAI Lab All rights reserved. Case 1.1 : Wisdom

    of the Crowd ut How? A mechanism that allows users to vote content helpfulness or likeness Twitter StackExchange Amazon
  9. ɹConfidentialɹ© TDAI Lab All rights reserved. Case 1.1 : Wisdom

    of the Crowd ɾFew contents acquires the majority of the votes. ɾMost of the contents are unrated. → We need machine learning to estimate the quality of the unrated contents.
  10. ɹConfidentialɹ© TDAI Lab All rights reserved. Case 1.2 : Unsupervised

    Machine Learning Google: PageRank ranks web pages according to “importance” REV2 is an anomaly detection method based on rating behavior, detecting “fraudulent” users. – Every webpage is a node, and every web-link is an edge. – Every reviewer is a node, and every rating is an edge. REV2: Fraudulent User Prediction in Rating Platforms
  11. ɹConfidentialɹ© TDAI Lab All rights reserved. Case 1.3 : Unsupervised

    Machine Learning ut How? Youtube Spotify Netflix Collaborative Filtering is based on "If the interests of a user A and user B are similar, user A will behave similarly to user B in the future". Create different rankings from people to people.
  12. ɹConfidentialɹ© TDAI Lab All rights reserved. Case 1.4 : Supervised

    Machine Learning Prediction Airbnb: Improving Deep Learning For Airbnb Search Helpfulness Prediction aims to predict helpfulness score based on review text, metadata.
  13. ɹConfidentialɹ© TDAI Lab All rights reserved. Case 1.5 : Supervised

    Machine Learning Facebook: Deepfake Detection Fake Detection KDD 2020 Fake News Detection Assessing Human Value Hiring Credibility Insurance fee Future Crime
  14. ɹConfidentialɹ© TDAI Lab All rights reserved. Are They Truly Estimating

    the Quality of the Contents?
  15. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 15 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • How to Fight Against Bias • Summary
  16. ɹConfidentialɹ© TDAI Lab All rights reserved. Technique Summary Voting Mechanism

    Unsupervised Learning Supervised learning (review, Q&A, SNS) ɾGraph-Based Ranking ɾAnomaly Detection ɾCollaborative Filtering ɾLearning-to-Rank ɾHelpfulness Prediction ɾFake Detection Wisdom of the Crowd Machine Learning
  17. ɹConfidentialɹ© TDAI Lab All rights reserved. Wisdom of the Crowd

    Pros. Cons. ɾEasy to implement ɾMost of the contents are unrated. ɾPopularity ≠ Quality
 ※Wisdom of the crowd assumes the independence of the decision.
  18. ɹConfidentialɹ© TDAI Lab All rights reserved. Cognitive Bias ɾ Content

    topic describes what the content is about; ɾ Content quality refers to what the voter assesses the quality of the content to be; ɾ Presentation order captures the position of the content in a list-style webpage; ɾ Social influence in terms of prior votes as seen by the voter; ɾ Author reputation reflects the online reputation of the user who created the content. Burghardt et al.* suggests a number of factors that may affect votes on content: *1 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173610
  19. ɹConfidentialɹ© TDAI Lab All rights reserved. Cognitive Bias ɾ Content

    topic describes what the content is about; ɾ Content quality refers to what the voter assesses the quality of the content to be; ɾ Presentation order captures the position of the content in a list-style webpage; ɾ Social influence in terms of prior votes as seen by the voter; ɾ Author reputation reflects the online reputation of the user who created the content. Burghardt et al.* suggests a number of factors that may affect votes on content: *1 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173610 ɹɹ Independent judges are violated → Decrease in diversity of opinions → Less quality of wisdom of the crowd
  20. ɹConfidentialɹ© TDAI Lab All rights reserved. Secondary Bias Winner-cycle Bias

    Early Bird Bias ɹɹ popularity and quality doesn’t have high correlation
  21. ɹConfidentialɹ© TDAI Lab All rights reserved. Technique Summary Voting Mechanism

    Unsupervised Learning Supervised learning (review, Q&A, SNS) ɾGraph-Based Ranking ɾAnomaly Detection ɾCollaborative Filtering ɾLearning-to-Rank ɾHelpfulness Prediction ɾFake Detection Wisdom of the Crowd Machine Learning
  22. ɹConfidentialɹ© TDAI Lab All rights reserved. Unsupervised Learning Pros. ɾCertain

    degree of performance even without annotated data
 ※ if the assumption is valid 
 Cons. ɾRequires domain specific knowledge ɾad hoc maintenance for improving performance

  23. ɹConfidentialɹ© TDAI Lab All rights reserved. The limitation of Unsupervised

    learning ex.) K-Means Clustering : assumes features mapped nearly have similar pattern 
 → easy to fail when modeling high-dimensional data
 
 PageRank: pages that are linked to important pages are important 
 → winner-cycle bias 
 ɹɹ Assumption of algorithm internally contains the bias of developer → If assumptions are different from real-world behavior, 
 the algorithm sometimes behaves unfairly.
  24. ɹConfidentialɹ© TDAI Lab All rights reserved. Technique Summary Voting Mechanism

    Unsupervised Learning Supervised learning (review, Q&A, SNS) ɾGraph-Based Ranking ɾAnomaly Detection ɾCollaborative Filtering ɾLearning-to-Rank ɾHelpfulness Prediction ɾFake Detection Wisdom of the Crowd Machine Learning
  25. ɹConfidentialɹ© TDAI Lab All rights reserved. Supervised Learning Pros. ɾDomain-invariant

    universal technique 
 (no need domain specific knowledge) ɾAdaptive to user behavior 
 Cons. ɾRequires sufficient annotated data ɾvulnerable to “Data Bias”
  26. ɹConfidentialɹ© TDAI Lab All rights reserved. What is Learning-to Rank

    Traditionally, Learning to Rank (LTR) is supervised through annotated datasets: Relevance annotations for query-document pairs provided by human judges. ɾQueries, representing queries users will issue, ɾDocuments, per query a preselected set of documents to be ranked, ɾRelevance Labels, indicating relevance/preference per document-query pair.
  27. ɹConfidentialɹ© TDAI Lab All rights reserved. Supervised Learning-to Rank However,

    over time several limitations of this approach have become apparent.
 ɾexpensive to make (Qin and Liu, 2013; Chapelle and Chang, 2011). ɾunethical to create in privacy-sensitive settings (Wang et al., 2016a). ɾimpossible for small scale problems, e.g., personalization. ɾstationary, cannot capture future changes in relevancy (Lefortier et al., 2014). ɾnot necessarily aligned with actual user preferences (Sanderson, 2010),
 i.e., annotators and users often disagree
  28. ɹConfidentialɹ© TDAI Lab All rights reserved. Supervised Learning-to Rank Traditionally,

    learning to rank is supervised through annotated datasets: Relevance annotations for query-document pairs provided by human judges. 
 However, over time several limitations of this approach have become apparent.
 ɾexpensive to make (Qin and Liu, 2013; Chapelle and Chang, 2011). ɾunethical to create in privacy-sensitive settings (Wang et al., 2016a). ɾimpossible for small scale problems, e.g., personalization. ɾstationary, cannot capture future changes in relevancy (Lefortier et al., 2014). ɾnot necessarily aligned with actual user preferences (Sanderson, 2010),
 i.e., annotators and users often disagree ɹɹ Can we naively use click as annotation? ɹɹ The model will be biased if you are not careful
  29. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 29 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • How to Fight Against Bias • Summary
  30. ɹConfidentialɹ© TDAI Lab All rights reserved. Bias In Bias Out

    Trained w/ Biased Data Clean Algorithm Biased Algorithm BERT image is cited from https://arxiv.org/pdf/2004.06660.pdf
  31. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 31 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • (Case1) Machine Learning against human • (Case2) Learning to Rank • (Case3) Recommendation • How to Fight Against Bias • Summary
  32. ɹConfidentialɹ© TDAI Lab All rights reserved. Case2.1 Income predictions ɾtraining

    a basic classifier that can predict whether or not a person's income is larger than $50K a year. ɾIt is important to note sensitive attributes(race and sex) are not part of the features used for training the model. https://godatadriven.com/blog/towards-fairness-in-ml-with-adversarial-networks/
  33. ɹConfidentialɹ© TDAI Lab All rights reserved. Case2.1 Income predictions ɾtraining

    a basic classifier that can predict whether or not a person's income is larger than $50K a year. ɾIt is important to note sensitive attributes(race and sex) are not part of the features used for training the model. ɹɹ Our classifier did not have access to the race and sex attributes. But still we have model biased against women and black people. ɹɹ What caused our classifier to behave this way?
  34. ɹConfidentialɹ© TDAI Lab All rights reserved. Difficulty of removing bias

    ɾModel can indirectly learn these biases, for example, through other characteristics (e.g., education level, zip-code of residence, religion, career, and preference of music). ɾAs a result, we end-up with the unfair predictions observed in previous section, even after having removed the race and sex attributes.
  35. ɹConfidentialɹ© TDAI Lab All rights reserved. Case2.2 : Racial Bias

  36. ɹConfidentialɹ© TDAI Lab All rights reserved. Case2.3 : Sexual Bias

  37. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 37 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • (Case1) Machine Learning against human • (Case2) Learning to Rank • (Case3) Recommendation • How to Fight Against Bias • Summary
  38. ɹConfidentialɹ© TDAI Lab All rights reserved. Supervised Learning-to Rank Traditionally,

    learning to rank is supervised through annotated datasets: Relevance annotations for query-document pairs provided by human judges. 
 However, over time several limitations of this approach have become apparent.
 ɾexpensive to make (Qin and Liu, 2013; Chapelle and Chang, 2011). ɾunethical to create in privacy-sensitive settings (Wang et al., 2016a). ɾimpossible for small scale problems, e.g., personalization. ɾstationary, cannot capture future changes in relevancy (Lefortier et al., 2014). ɾnot necessarily aligned with actual user preferences (Sanderson, 2010),
 i.e., annotators and users often disagree ɹɹ Can we naively use click as annotation? ɹɹ The model will be biased if you are not careful
  39. ɹConfidentialɹ© TDAI Lab All rights reserved. Presentation Bias Bias on

    the Web | June 2018 | Communications of the ACM
  40. ɹConfidentialɹ© TDAI Lab All rights reserved. Presentation Bias Bias on

    the Web | June 2018 | Communications of the ACM
  41. ɹConfidentialɹ© TDAI Lab All rights reserved. Position Based Model (PBM)

    ℙ(C = 1 ∣ q, d, k) Click = ℙ(E = 1 ∣ k) Examination ⋅ ℙ(R = 1 ∣ q, d) Relevance Examination 👀 Relevance💰 Click ✅ O=1 R=1 Y=1 O=1 R=0 Y=0 — — — O=0 R=1 Y=0 O=0 R=1 Y=0 O=0 R=0 Y=0 We often do not know the true relevance labels , but can only observe clicks: y (di) Unbiased Learning-to-Rank with Biased Feedback
  42. ɹConfidentialɹ© TDAI Lab All rights reserved. Position Based Model (PBM)

    ℙ(C = 1 ∣ q, d, k) Click = ℙ(E = 1 ∣ k) Examination ⋅ ℙ(R = 1 ∣ q, d) Relevance Examination 👀 Relevance💰 Click ✅ O=1 R=1 Y=1 O=1 R=0 Y=0 — — — O=0 R=1 Y=0 O=0 R=1 Y=0 O=0 R=0 Y=0 ɹɹ • A click on document is a biased and noisy indicator that is relevant • A missing click does not necessarily indicate non-relevance. di di
  43. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 43 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • (Case1) Machine Learning against human • (Case2) Learning to Rank • (Case3) Recommendation • How to Fight Against Bias • Summary
  44. ɹConfidentialɹ© TDAI Lab All rights reserved. Recommendation https://medium.com/@lz2576/a-first-look-at-recommendation-system-with-matrix-factorization-and-neural-nets-7e21e54295c ɾCollaborative Filtering

    is based on "If the interests of a user A and user B are similar, 
 user A will behave similarly to user B in the future". ɾTrying to predict the missing value in the matrix
  45. ɹConfidentialɹ© TDAI Lab All rights reserved. Exposure Model P (Yu,i

    = 1) click prob = P (Ou,i = 1) exposure prob ⋅ P (Ru,i = 1) relevance level Recommendation Exposure Relevance Click Yes O=1 R=1 Y=1 Yes O=1 R=0 Y=0 — — — — No O=0 R=1 Y=0 No O=0 R=1 Y=0 No O=0 R=0 Y=0 Modeling User Exposure in Recommendation Liang et al. WWW 2016
  46. ɹConfidentialɹ© TDAI Lab All rights reserved. Exposure Model Liang et

    al. WWW 2016 P (Yu,i = 1) click prob = P (Ou,i = 1) exposure prob ⋅ P (Ru,i = 1) relevance level Recommendation Exposure Relevance Click Yes O=1 R=1 Y=1 Yes O=1 R=0 Y=0 — — — — No O=0 R=1 Y=0 No O=0 R=1 Y=0 No O=0 R=0 Y=0 ɹɹ • A click on item is a biased and noisy indicator that is relevant • A missing click does not necessarily indicate non-relevance. i i
  47. ɹConfidentialɹ© TDAI Lab All rights reserved. Missing Not At Random

    ɾThe observations are biased by self-selection bias. → User like to click the items they like, not the other way around
  48. ɹConfidentialɹ© TDAI Lab All rights reserved. Algorithms amplify the bias

    Y-axis: Cumulative % of exposure X-axis: Cumulative % of producers (ranked in increasing exposure) Google Local: Top 20 % businesses got ~80 % of total exposure Lastfm: Bottom 60 % artists got ~20 % of total exposure
  49. ɹConfidentialɹ© TDAI Lab All rights reserved. Filter Bubble Users are

    unknowingly only exposed to information that they already believed and like, as if they were trapped in a "bubble “ →less diversity
  50. ɹConfidentialɹ© TDAI Lab All rights reserved. Recap ɾBias In Bias

    Out ɾMost web systems are trained with user implicit feedback. ɹˠ Click can only be done on things that are shown ɾML learn to reinforce their own bias ɹˠ less diversity
  51. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 51 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • How to Fight Against Bias • Summary
  52. ɹConfidentialɹ© TDAI Lab All rights reserved. Bias In Bias Out

    Trained w/ Biased Data Clean Algorithm BERT image is cited from https://arxiv.org/pdf/2004.06660.pdf Debiasing Algorithm Clean Algorithm
  53. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 53 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • How to Fight Against Bias • IPS • Adversarial Training • Summary
  54. ɹConfidentialɹ© TDAI Lab All rights reserved. Inverse Propensity Scoring If

    no click noise is present, this provides an unbiased estimate: P (Ru,i = 1) = P (Yu,i = 1) P (Ou,i = 1) P(R = 1 ∣ q, d) = P(C = 1 ∣ q, d, k) P(E = 1 ∣ k) Search engine(PBM) : Recommendation(Exposure Model):
  55. ɹConfidentialɹ© TDAI Lab All rights reserved. Inverse Propensity Scoring Handling

    Position Bias for Unbiased Learning to Rank in Hotels Search P(R = 1 ∣ q, d) = P(C = 1 ∣ q, d, k) P(E = 1 ∣ k) Difficult to estimate Requiring randomized experiments
  56. ɹConfidentialɹ© TDAI Lab All rights reserved. Inverse Propensity Scoring Handling

    Position Bias for Unbiased Learning to Rank in Hotels Search P(R = 1 ∣ q, d) = P(C = 1 ∣ q, d, k) P(E = 1 ∣ k) Difficult to estimate Requiring randomized experiments If a site is clicked 5 times less visible, let the model learn 5 times more weightedɹ
  57. ɹConfidentialɹ© TDAI Lab All rights reserved. Inverse Propensity Scoring P

    (Ru,i = 1) = P (Yu,i = 1) P (Ou,i = 1)?? Sometimes substituted by propensity score amortized over user or item: P (Ou,* = 1), P (O*,i = 1) P(R = 1 ∣ q, d) = P(C = 1 ∣ q, d, k) P(E = 1 ∣ k) Almost impossible to estimate user-wise propensity score PBM also makes strong assumptions that “examination propensity only depends on position” →ɹSubstantial parameter reduction P (E = 1 ∣ u, k, d) Human Bias?
  58. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 58 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • How to Fight Against Bias • IPS • Adversarial Training • Summary
  59. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 59 Generative adversarial network
  60. ɹConfidentialɹ© TDAI Lab All rights reserved. Recap: Generative Adversarial Network

    https://poloclub.github.io/ganlab/
  61. ɹConfidentialɹ© TDAI Lab All rights reserved. Case1.3 Income predictions ɾtraining

    a basic classifier that can predict whether or not a person's income is larger than $50K a year. ɾIt is important to note sensitive attributes(race and sex) are not part of the features used for training the model.
  62. ɹConfidentialɹ© TDAI Lab All rights reserved. Typical Machine Learning ɾTask

    specific model are trained.
  63. ɹConfidentialɹ© TDAI Lab All rights reserved. Fairness with adversarial training

    Based on Learning to Pivot with Adversarial Networks FRAGE: Frequency-Agnostic Word Representation Task specific model + Discriminator ɾDiscriminator tries to predict sensitive features. ɾTask specific model need to fool the discriminator
  64. ɹConfidentialɹ© TDAI Lab All rights reserved. Fair income predictions Task

    specific model + Discriminator ɾDiscriminator tries to predict sensitive features. ɾTask specific model need to fool the discriminator
  65. ɹConfidentialɹ© Japan Data Science Consortium. All right reserved. ɹConfidentialɹ© TDAI

    Lab All rights reserved. 65 Agenda • Prologue • Technique to Measure the Quality of the Contents • Pros/Cons of the Techniques • Biased Model Case Study • How to Fight Against Bias • Summary
  66. ɹConfidentialɹ© TDAI Lab All rights reserved. Trusted AI ML is

    necessary in the age of information overload. Developers must build technology people can trust. → using human bias to “reduce” bias Demand for: ɾFairness ɾAccountability ɾTransparency ɾRobustness
  67. ɹConfidentialɹ© TDAI Lab All rights reserved. Trusted AI ← Explainability

    / Fairness ← Fairness ← Robustness ← Trust ← Trust ← Fairness
  68. ɹConfidentialɹ© TDAI Lab All rights reserved. Case Study:Babysitter Risk Rating

    Common Pitfalls for Studying the Human Side of Machine Learning Neurips 2018 Xcorp launches a new service that uses social media data to predict whether a babysitter candidate is likely to abuse drugs or exhibit other undesirable tendencies (e.g. aggressiveness, disrespectfulness, etc.) Using computational techniques, Xcorp will produce a score to rate the riskiness of the candidates. Candidates must opt in to being scored when asked by a potential employer. This product produces a rating of the quality of the babysitter candidate from 1-5 and displays this to the hiring parent. What would it mean for this system to be fair? ɹ
  69. ɹConfidentialɹ© TDAI Lab All rights reserved. https://www.washingtonpost.com/technology/2018/11/16/wanted-perfect- babysitter-must-pass-ai-scan-respect-attitude/

  70. ɹConfidentialɹ© TDAI Lab All rights reserved. Pretrained Model Age ɾRecent

    NLP progress(2018~) is based on pretrained models trained with huge corpus. ɾThe risk of using and trusting those models blindly is increasing ɾWhat if they are biased ??
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