Skip to main content
UploadLanguage (EN)Support
BusinessMobileSocial MediaMarketingTechnologyArt & PhotosCareerDesignEducationPresentations & Public SpeakingGovernment & NonprofitHealthcareInternetLawLeadership & ManagementAutomotiveEngineeringSoftwareRecruiting & HRRetailSalesServicesScienceSmall Business & EntrepreneurshipFoodEnvironmentEconomy & FinanceData & AnalyticsInvestor RelationsSportsSpiritualNews & PoliticsTravelSelf ImprovementReal EstateEntertainment & HumorHealth & MedicineDevices & HardwareLifestyle
SH
Uploaded bySatoshi Hara
PDF, PPTX104,501 views

機械学習モデルの判断根拠の説明

The document discusses the rights of data subjects under the EU GDPR, particularly regarding automated decision-making and profiling. It outlines conditions under which such decisions can be made, emphasizing the need for measures that protect the data subjects' rights and freedoms. Additionally, it includes references to various machine learning and artificial intelligence interpretability frameworks and studies.

Technology◦
Download as PDF, PPTX
1 20 2018/12/21
n • ~2013.3, PhD@ , • 2013.4~2016.3, @IBM • 2016.4~2017.8, @ ERATO, NII • 2017.9~, @ , n • ECML’11 AISTATS’15,17 • AISTATS’18 AAAI’17,18 ongoing 2
• • • • AISTATS’18 • AAAI’17,18 3
AI n • n • 4 …
AI n • n • 5 XX XX
AI n • AI AI AI AI n • AI • n 6
n • • • AI • • 7
8
EU GDPR n GDPR-22 1. The data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her. 2. Paragraph 1 shall not apply if the decision: is necessary for entering into, or performance of, a contract between the data subject and a data controller; is authorised by Union or Member State law to which the controller is subject and which also lays down suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests; or is based on the data subject’s explicit consent. 3. In the cases referred to in points (a) and (c) of paragraph 2, the data controller shall implement suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests, at least the right to obtain human intervention on the part of the controller, to express his or her point of view and to contest the decision. 4. Decisions referred to in paragraph 2 shall not be based on special categories of personal data referred to in Article 9(2)1), unless point (a) or (g) of Article 9(2) applies and suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests are in place. 9
n 2016 • ICML, NIPS n • , , Vol.33, No.3, pages 366--369, 2018. • , Qiita 10
n AI 11 Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI) https://ieeexplore.ieee.org/document/8466590/ 7 SCOPUS, IEEExplore, ACM Digital Library, Google Scholar, Citeseer Library, ScienceDirect, arXiv “intelligible”, “interpretable”, “transparency”, “black box”, “understandable”, “comprehensible”, “explainable” AI “Artificial Intelligence”, “Intelligent system”, “Machine learning”, “deep learning”, “classifier” , “decision tree”
• • • • AISTATS’18 • AAAI’17,18 12
n • n • 1. • 2. • 3. • 4. • … 13
n n / • n • • n “ ” • 14
1. • 2. • 3. • 15
n n Why Should I Trust You?: Explaining the Predictions of Any Classifier, KDD'16 [Python LIME; R LIME] n A Unified Approach to Interpreting Model Predictions, NIPS'17 [Python SHAP] n Anchors: High-Precision Model-Agnostic Explanations, AAAI'18 [Python Anchor] n Understanding Black-box Predictions via Influence Functions, ICML’17 [Python influence-release] 16
n n Born Again Trees n Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach, AISTATS'18 [Python defragTrees] 17
n n [Python+Tensorflow saliency; DeepExplain] • Striving for Simplicity: The All Convolutional Net (GuidedBackprop) • On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation (Epsilon-LRP) • Axiomatic Attribution for Deep Networks (IntegratedGrad) • SmoothGrad: Removing Noise by Adding Noise (SmoothGrad) • Learning Important Features Through Propagating Activation Differences (DeepLIFT) 18
19
n n Why Should I Trust You?: Explaining the Predictions of Any Classifier, KDD'16 [Python LIME; R LIME] n A Unified Approach to Interpreting Model Predictions, NIPS'17 [Python SHAP] n Anchors: High-Precision Model-Agnostic Explanations, AAAI'18 [Python Anchor] n Understanding Black-box Predictions via Influence Functions, ICML’17 [Python influence-release] 20
n n • LIME, SHAP, Anchor n • influence 21
LIME n Why Should I Trust You?: Explaining the Predictions of Any Classifier, KDD'16 [Python LIME; R LIME] • • 22
LIME 23
LIME n 2 • One-Hot • n LIME • Adult One-Hot 24
LIME n ! !′ • LIME !′ !# ∈ {0, 1}* + !, # + n -(!) !0 • - ! ≈ 2 !# ≔ 40 + 46!# for !# ∈ NeighborOf(!0 # ) • 4 4, 25
LIME n !(#) #% • ! # ≈ ' #( ≔ *% + *,#( for #( ∈ NeighborOf(#% ( ) n * • min : ∑ <,<> ∈? @AB C ! C − ' C( E s.t. * % ≤ G * % @AB C #% H #% 26
LIME n n • vs • → LIME 27
SHAP n A Unified Approach to Interpreting Model Predictions, NIPS'17 [Python SHAP] • • LIME 28
SHAP n 1 n 2 29
SHAP n SHAP LIME • Adult One-Hot n . “ ” SHAP “ ” 30
SHAP n SHAP = LIME • ! "# = %& + %("# = %& + ∑* %*"* # 3 n 1. + " = ! "# • 31
SHAP n SHAP = LIME • ! "# = %& + %("# = %& + ∑* %*"* # 3 n 2. "* # = 0 ⇒ %* = 0 • 32
SHAP n SHAP = LIME • ! "# = %& + %("# = %& + ∑* %*"* # 3 n 3. + " = +, "# + +′ +, # "# − +, # "# ∖ 0 ≥ +, "# − +, "# ∖ 0 ⇒ %* +# ≥ %* + • + +′ "* # +′ + +# "* # + 33
SHAP n SHAP = LIME • ! "# = %& + %("# = %& + ∑* %*"* # SHAP n 3 • %* = ∑+⊆-. + ! 01 + 12 ! 0! (4- 5 − 4-(5 ∖ 8)) • Shapley Value 34
SHAP n LIME • min $ ∑&∈( ) * +, * − . * / • ) * = 123 1 456678 & & (1 2|&|) n • Linear SHAP: • Tree SHAP: • Deep SHAP: DeepLIFT 35
Anchor n Anchors: High-Precision Model-Agnostic Explanations, AAAI'18 [Python Anchor] • 36
Anchor 37
Anchor n Anchor LIME • Adult One-Hot n • Anchor + 38
Anchor n “ + • 39
Anchor n ! • " ! ! " = 1 n %(⋅ |!) ! n ! =Anchor • Anchor ! * " + ,- * ! 1. / 0. 1 ≥ +, ! " = 1 Anchor ! " 40
Anchor n !" # $ 1& ' (& ) ≥ + • 1 − - Pr !" # $ 1& ' (& ) ≥ + ≥ 1 − - 41
Anchor n ! = ! • ! • max % &' ( [!(+)] s.t. Pr &' + ! 11 ( 21 3 ≥ 5 ≥ 1 − 7 n 1. n 2. • Pr &' + ! 11 ( 21 3 ≥ 5 1 − 7 ! 42
n n • LIME, SHAP, Anchor n • influence 43
influence n Understanding Black-box Predictions via Influence Functions, ICML’17 [Python influence-release] n ("′, %′) " 44
influence n ("′, %′) " n % = (("; *+), *+ *+ = argmin 2∈4 5 67(8,9)∈: ;(<; +) *+=6> = argmin 2∈4 5 6∈: ?@A 6B6> ;(<; +) n <> = ("′, %′) *+ • C+=6> − C+ *+=6> − *+ <> 45 <> = ("′, %′)
influence n !" = (%′, (′) *+ • ,+-." − ,+ *+-." − *+ !" n • !" = (%′, (′) ,+-." • n influence • ,+-." − ,+ ,+-." − ,+ ≈ − 1 2 345 -6 78(!"; ,+) 46
influence n Data Poisoning • • 47
influence n • 48 This looks like that: deep learning for interpretable image recognition, arxiv: 1806.10574.
n n • LIME • SHAP • Anchor n • influence 49
n • Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking, KDD’17 • 50 30 200 20 200 30 300 ⭕ ⭕ … …
n • Generating Visual Explanations, ECCV‘16 • 51
52
n n Born Again Trees n Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach, AISTATS'18 [Python defragTrees] 53
BATrees n Born Again Trees • • n • ! ! • 54
n • BATrees • defragTrees RandomForest 55
56
n n [Python+Tensorflow saliency; DeepExplain] • Striving for Simplicity: The All Convolutional Net (GuidedBackprop) • On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation (Epsilon-LRP) • Axiomatic Attribution for Deep Networks (IntegratedGrad) • SmoothGrad: Removing Noise by Adding Noise (SmoothGrad) • Learning Important Features Through Propagating Activation Differences (DeepLIFT) 57
n • 58 DNN
n • 59 DNN
n • 60 DNN
n DNN 61
n • → • → 62
n ! = # $ n $ n [Simonyan et al., arXiv’14] $% &' ( &() • → → *+ , *,- → • → → &' ( &() → 63
n [Simonyan et al., arXiv’14] !" #$ % #%& n • GuidedBP [Springenberg et al., arXiv’14] back propagation • LRP [Bach et al., PloS ONE’15] • IntegratedGrad [Sundararajan et al., arXiv’17] • SmoothGrad [Smilkov et al., arXiv’17] • DeepLIFT [Shrikumar et al., ICML’17] 64
n n [Python+Tensorflow saliency; DeepExplain] • Striving for Simplicity: The All Convolutional Net (GuidedBackprop) • On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation (Epsilon-LRP) • Axiomatic Attribution for Deep Networks (IntegratedGrad) • SmoothGrad: Removing Noise by Adding Noise (SmoothGrad) • Learning Important Features Through Propagating Activation Differences (DeepLIFT) 65
• • • • AISTATS’18 • AAAI’17,18 66
n AISTATS’18 • n AAAI’17,18 • 67
defragTrees n Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach, AISTATS'18 [Python defragTrees] • • n 69 when Relationship ≠ Not-in-family, Wife Capital Gain < 7370 when Relationship ≠ Not-in-family Capital Gain >= 7370 when Relationship ≠ Not-in-family, Unmarried Capital Gain < 5095 Capital Loss < 2114 when Relationship = Not-in-family Country ≠ China, Peru Capital Gain < 5095 when Relationship ≠ Not-in-family Country ≠ China Capital Gain < 5095 when Relationship ≠ Not-in-family Capital Gain >= 7370 … …
n n • 70 y = XOR(x1 < 0.5, x2 < 0.5) + ✏
n n 71 2017 The State of Data Science & Machine LearningYour Year on Kaggle: Most Memorable Community Stats from 2016
n n 72 R
n • • n 73
defragTrees n 1. • !" #, % &) n 2. !"( #, % )) • !"( #, % )) ≈ !"(#, %|&) ) ≪ & !"( #, % )) n • ) • Factorized Asymptotic Bayesian (FAB) Inference 74 & )
n n • • • 75 D Synthetic 2 1000 1000 Spambase 57 1000 1000 MiniBooNE 50 5000 5000 Magic 11 5000 5000 Higgs 28 5000 5000 Energy 8 384 384
n 76
n 77
n • n • • 78
n AISTATS’18 • n AAAI’17,18 • 79
n Enumerate Lasso Solu/ons for Feature Selec/on, AAAI’17 [Python LassoVariants]. n Approximate and Exact Enumera/on of Rule Models, AAAI'18.
→ NO! n • 82
n • → • n • 83
n • n • → 84
n • n • → 85
n • n • → 86
n • n • 87
• •
Lasso Given: !", $" ∈ ℝ'×ℝ ) = 1, 2, … , . Find: / ∈ ℝ' s.t. !" 0 / ≈ $ () = 1, 2, … , .) / n • • Lasso ℓ5 /∗ = argmin = 1 2 >/ − $ @ + B / 5 • Lasso /∗ supp(/∗) = {) ∶ /" ∗ ≠ 0}
Lasso n • → Lasso n Lasso • → n
Lasso n ! ⊆ {$%, $', … , $)} Lasso Lasso ! = min 3 % ' 45 − 7 ' + 9 5 % s.t. supp 5 ⊆ ! Lasso ! Lasso ! < supp 5 = ! • 9 • 9 • $%, $', $= , $%, $', $> , $%, $>, $? , $%, $' , … Lasso !
Lawler !-best 1. " # 2. $ ∈ # " $ "& = " ∖ {$} Lasso("′) #′ (#&, "′) 3. 4.
Lawler !-best 1. " # 2. $ ∈ & ' $ '( = ' ∖ {$} Lasso('′) &′ (&(, '′) 3. 4. & = 56, 57, 58 ' = 56, 57, 59, 58, 5: &6 = 56, 57, 58 '6 = 56, 57, 59, 58, 5:
Lawler !-best 1. " # 2. $ ∈ & ' $ '( = ' ∖ {$} Lasso("′) #′ (#(, "′) 3. 4. "5 ( = 67, 68, 69, 6: "7 ( = 65, 68, 69, 6: "8 ( = 65, 67, 68, 6: "5 = 65, 67, 68, 69, 6: #5 = 65, 67, 69 "5 = 65, 67, 68, 69, 6:
Lawler !-best 1. " # 2. $ ∈ & " ' "( = " ∖ {'} -.//0(2′) &′ (&(, 2′) 3. 4. (#6 (= 78, 79, 7: , "6 ()"6 ( = 78, 7;, 79, 7: "8 ( = 76, 7;, 79, 7: "; ( = 76, 78, 7;, 7: "6 = 76, 78, 7;, 79, 7: #6 = 76, 78, 79 "6 = 76, 78, 7;, 79, 7:
Lawler !-best 1. " # 2. $ ∈ & " ' "( = " ∖ {'} -.//0(2′) &′ (&(, 2′) 3. 4. (#6 (= 78, 79, 7: , "6 ()"6 ( = 78, 7;, 79, 7: "8 ( = 76, 7;, 79, 7: "; ( = 76, 78, 7;, 7: "6 = 76, 78, 7;, 79, 7: (#8 (= 76, 7;, 79 , "8 () #6 = 76, 78, 79 "6 = 76, 78, 7;, 79, 7:
Lawler !-best 1. " # 2. $ ∈ & " ' "( = " ∖ {'} -.//0(2′) &′ (&(, 2′) 3. 4. (#6 (= 78, 79, 7: , "6 ()"6 ( = 78, 7;, 79, 7: "8 ( = 76, 7;, 79, 7: "; ( = 76, 78, 7;, 7: "6 = 76, 78, 7;, 79, 7: (#8 (= 76, 7;, 79 , "8 () (#; (= 76, 78, 7: , "; ()#6 = 76, 78, 79 "6 = 76, 78, 7;, 79, 7:
Lawler !-best 1. " # 2. $ ∈ # " $ "& = " ∖ {$} +,--.("′) #′ (#&, "′) 3. 4. (#3 &= 45, 46, 47 , "3 &) (#5 &= 43, 48, 46 , "5 &) (#8 &= 43, 45, 47 , "8 &) #5 = 45, 46, 47 "5 = 45, 48, 46, 47 #3 = 43, 45, 46 "3 = 43, 45, 48, 46, 47
Lawler !-best 1. " # 2. $ ∈ & ' $ '( = ' ∖ {$} Lasso("′) #′ (#(, "′) 3. 4. #5 = 65, 67, 68 "5 = 65, 67, 69, 68, 6: (#7 (= 65, 69, 68 , "7 () (#9 (= 65, 67, 6: , "9 () #7 = 67, 68, 6: "7 = 67, 69, 68, 6: "8 ( = 69, 68, 6: ": ( = 67, 69, 6: "; ( = 67, 69, 68 "7 = 67, 69, 68, 6: