Trusting machines with robust, unbiased and reproducible AI
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To trust a decision made by an algorithm, we need to know that it is reliable and fair, that it can be accounted for, and that it will cause no harm. We need assurance that it cannot be tampered with and that the system itself is secure. We need to understand the rationale behind the algorithmic assessment, recommendation or outcome, and be able to interact with it, probe it – even ask questions. And we need assurance that the values and norms of our societies are also reflected in those outcomes. Learn about how bias can take root in machine learning algorithms and ways to overcome it. From the power of open source, to tools built to detect and remove bias in machine learning models, there is a vibrant ecosystem of contributors who are working to build a digital future that is inclusive and fair. Learn how to achieve AI fairness, robustness, explainability and accountability. You can become part of the solution.
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Trusting machines with robust, unbiased and reproducible AI
- 1. Trusting machines with robust, unbiased and reproducible AI — Dr. Margriet Groenendijk Data & AI Developer Advocate Data Science London Meetup November 25, 2019
- 2. About me @MargrietGr Water Use Efficiency = Photosynthesis / Transpiration
- 3. About me @MargrietGr Artificial Intelligence (Big) Data Data Science Deep Learning Machine Learning
- 4. @MargrietGr
- 5. LF AI Day - Paris September 2019 @MargrietGr https://lfai.foundation
- 7. GANs – Imaginary Celebrities @MargrietGr https://arxiv.org/abs/1710.10196
- 9. @MargrietGr
- 10. @MargrietGr
- 13. "AI is the state of the art of computers, but calling it intelligence bothers me” @stevewoz #GOTOcph @MargrietGr
- 14. Machine learning Algorithm selection Deep learning Neural network design Natural Language Processing Interactions between computers and human languages Artificial intelligence Systems architecture @MargrietGr
- 15. AI is used in many decision making applications Credit Employment Admission HealthcareSentencing @MargrietGr
- 17. Gender Shades Project Released February 2018 @MargrietGr
- 22. What does it take to trust a decision made by a machine? (Other than that it is 99% accurate)? @MargrietGr
- 23. Is it fair? Is it accountable? What does it take to trust a decision made by a machine? (Other than that it is 99% accurate)? Did anyone tamper with it? #21, #32, #93 #21, #32, #93 Is it easy to understand? @MargrietGr
- 24. Models @MargrietGr Build your own from scratch Use an open source package to build your own Or use pre-trained models
- 25. @MargrietGr What do you know about the model?
- 26. What do you know about the model? @MargrietGr Who build it? What data was used? How was it deployed?
- 28. Deploy Docker Red Hat Openshift Kubernetes Locally @MargrietGr https://developer.ibm.com/exchanges/models/all/max-object-detector/ Use cURL Node-RED flow Codepen Serverless app Jupyter notebook Train Collect data Train model Rebuild Model- Serving Microservice
- 30. Models Model Asset Exchange (MAX) https://ibm.biz/model-exchange Acumos marketplace https://marketplace.acumos.org Model Zoo https://modelzoo.co Google AI Hub https://cloud.google.com/ai-hub Tensorflow https://www.tensorflow.org/resources/models-datasets@MargrietGr Build your own from scratch Use an open source package to build your own Or use pre-trained models
- 31. @MargrietGr How does it work?
- 34. Is it fair? Is it accountable? What does it take to trust a decision made by a machine? Did anyone tamper with it? #21, #32, #93 #21, #32, #93 Is it easy to understand? @MargrietGr
- 35. Misclassification Adversarial machine learning Adversarial machine learning can be used to “trick” machine learning models into providing incorrect predictions https://www.ibm.com/blogs/research/2018/04/ai-adversarial-robustness-toolbox/
- 39. Adversarial threats to AI Evasion attacks Performed at test time Perturb inputs with noise Model fails to predict correctly Undetectable by humans
- 40. Adversarial threats to AI Poisoning attacks Performed at training time Insert poisoned sample in training data Use backdoor later
- 41. ART Adversarial Robustness 360 Toolbox ↳ (ART) https://github.com/IBM/adversarial-robustness-toolbox 41 Toolbox Evasion attacks Defense methods Detection methods Robustness metrics https://art-demo.mybluemix.net/
- 42. State-of-the-art methods for attacking and defending classifiers @MargrietGr Evasion attacks • FGSM • JSMA • BIM • PGD • Carlini & Wagner • DeepFool • NewtonFool • Universal perturbation Evasion defences • Feature squeezing • Spatial smoothing • Label smoothing • Adversarial training • Virtual adversarial training • Thermometer encoding • Gaussian data augmentation Poisoning detection • Detection based on clustering activations • Proof of attack strategy Evasion detection • Detector based on inputs • Detector based on activations Robustness metrics • CLEVER • Empirical robustness • Loss sensitivity Unified model API • Training • Prediction • Access to loss and prediction gradients https://adversarial-robustness-toolbox.readthedocs.io/en/latest/
- 44. Fast Gradient Method modifies images so that the confidence of the prediction is reduced @MargrietGr
- 45. Fast Gradient Method modifies images so that the confidence of the prediction is reduced @MargrietGr
- 46. @MargrietGr
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- 48. @MargrietGr
- 49. Is it fair? Is it accountable? What does it take to trust a decision made by a machine? Did anyone tamper with it? #21, #32, #93 #21, #32, #93 Is it easy to understand? @MargrietGr
- 50. Criminal Justice System Risk scores using Northpointe’s COMPAS algorithm. Defendants with low risk scores are released on bail. It falsely flagged black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing . @MargrietGr
- 51. AIF360 AI Fairness 360 ↳ (AIF360) https://github.com/IBM/AIF360 51 Toolbox Fairness metrics (70+) Fairness metric explanations Bias mitigation algorithms (10+) http://aif360.mybluemix.net/
- 52. AIF360 Demo @MargrietGr Disparate Impact Computed as the ratio of rate of favorable outcome for the unprivileged group to that of the privileged group. Compas (ProPublica recidivism) Predict a criminal defendant’s likelihood of reoffending. Protected Attributes: - Sex, privileged: Female, unprivileged: Male - Race, privileged: Caucasian, unprivileged: Not Caucasian
- 53. Reweighing Weights the examples in each (group, label) combination differently to ensure fairness before classification. @MargrietGr
- 54. Reject Option Based Classification algorithm applied Changes predictions from a classifier to make them fairer. Provides favorable outcomes to unprivileged groups in a confidence band around the decision boundary with the highest uncertainty. @MargrietGr
- 55. Is it fair? Is it accountable? What does it take to trust a decision made by a machine? Did anyone tamper with it? #21, #32, #93 #21, #32, #93 Is it easy to understand? @MargrietGr
- 56. AIX360 AI Explainability 360 ↳ (AIX360) https://github.com/IBM/AIX360 Toolbox Local post-hoc Global post-hoc Directly interpretable http://aix360.mybluemix.net @MargrietGr
- 57. AIX360: Different Ways to explain End users/customers (trust) Doctors: Why did you recommend this treatment? Customers: Why was my loan denied? Teachers: Why was my teaching evaluated in this way? @MargrietGr
- 58. AIX360: Different Ways to explain End users/customers (trust) Doctors: Why did you recommend this treatment? Customers: Why was my loan denied? Teachers: Why was my teaching evaluated in this way? Gov’t/regulators (compliance, safety) Prove to me that you didn't discriminate. @MargrietGr
- 59. AIX360: Different Ways to explain End users/customers (trust) Doctors: Why did you recommend this treatment? Customers: Why was my loan denied? Teachers: Why was my teaching evaluated in this way? Gov’t/regulators (compliance, safety) Prove to me that you didn't discriminate. Developers (quality, “debuggability”) Is our system performing well? How can we improve it? @MargrietGr
- 60. @MargrietGr
- 61. http://aix360.mybluemix.net/FICO Explainable Machine Learning Challenge dataset @MargrietGr Use the information about the applicant in their credit report to predict whether they will make timely payments over a two-year period
- 62. @MargrietGr ExternalRiskEstimate is an important feature positively correlated with good credit risk. The jumps in the plot indicate that applicants with above average ExternalRiskEstimate (the mean is 72) get an additional boost.
- 63. @MargrietGr
- 64. @MargrietGr
- 65. @MargrietGr
- 66. Is it fair? Is it accountable? What does it take to trust a decision made by a machine? (Other than that it is 99% accurate)? Did anyone tamper with it? #21, #32, #93 #21, #32, #93 Is it easy to understand? @MargrietGr
- 67. FAIRNESS EXPLAINABILITYROBUSTNESS LINEAGE Trusted AI Lifecycle through Open Source Adversarial Robustness 360 ↳ (ART) AI Fairness 360 ↳ (AIF360) AI Explainability 360 ↳ (AIX360) github.com/IBM/adversa rial-robustness-toolbox art-demo.mybluemix.net github.com/IBM/AIF360 aif360.mybluemix.net • github.com/IBM/AIX360 aix360.mybluemix.net In the works! Is it fair? Is it easy to understand? Is it accountable? Did anyone tamper with it?
- 68. Machine learning Algorithm selection Deep learning Neural network design Natural Language Processing Interactions between computers and human languages Artificial intelligence Systems architecture @MargrietGr
- 70. @MargrietGr Output Credit Risk Model Train Algorithm John X § credit_score=800 § age=25 § income=$900,000 § works in Oil & Gas Historical Loans Label No Risk Example: credit risk
- 71. @MargrietGr No Risk Output Risk James Y § credit_score=900 § age=55 § income=$1,200,000 § works in Insurance New Applicant Credit Risk Model Example: credit risk
- 72. @MargrietGr
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- 75. @MargrietGr 3RD PARTY IDE & FRAMEWORKS Watson OpenScale Automated Anomaly and Drift detection Business KPIs Watson Studio Watson Machine Learning 3RD PARTY RUNTIMES Build Deploy and run Operate trusted AI Fairness and Explainability Inputs for Continuous Evolution Accuracy Validation and Feedback SPSS Modeler Custom (Kubernetes etc.) Microsoft Azure ML Amazon Web Services Keras Pytorch Scikit-learn Spark ML Caffe2 … Free IBM Cloud account - https://ibm.biz/Bdz35F
- 76. Provision services Watson Studio Cloud Object Store Watson Machine Learning Watson OpenScale @MargrietGr
- 77. Provision services Setup a project Watson Studio Jupyter notebooks @MargrietGr
- 78. Provision services Setup a project Deploy pre-trained model Watson Studio Jupyter notebooks SparkML model Deploy as API Test API @MargrietGr
- 79. Provision services Setup a project Deploy pre-trained model Configure monitoring Watson Studio + OpenScale Jupyter notebooks + UI Set up datamart Subscribe to monitoring of deployed model - Quality and explainability - Fairness - Drift @MargrietGr
- 80. @MargrietGr
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- 89. Recap @MargrietGr Add trust by asking these questions Did anyone tamper with it? Is it fair? Is it easy to understand? Is it accountable? AI is a systems architecture with lots of moving parts It is not magic It is not intelligent Trusted AI is what I will focus on in 2020
- 90. Models Model Asset Exchange (MAX) https://ibm.biz/model-exchange Acumos marketplace https://marketplace.acumos.org Model Zoo https://modelzoo.co Google AI Hub https://cloud.google.com/ai-hub Tensorflow https://www.tensorflow.org/resources/models-datasets@MargrietGr Build your own… Or use pre-trained models
- 91. How do they work? @MargrietGr https://github.com/EthicalML/awesome-production-machine- learning#explaining-black-box-models-and-datasets
- 93. @MargrietGr Trusted AI Open-source! - EU https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai - Microsoft https://www.microsoft.com/en-us/ai/our-approach-to-ai - Google Cloud Explainable AI - Partnership on AI https://www.partnershiponai.org - Linux Foundation AI
- 94. Resources @MargrietGr Models ibm.biz/model-exchange Data ibm.biz/data-exchange IBM Cloud ibm.biz/BdzLdz Slides ibm.biz/slides-margriet Patterns and Tutorials https://developer.ibm.com ART https://art-demo.mybluemix.net AIF360 http://aif360.mybluemix.net AIX360 http://aix360.mybluemix.net