Trusting machines with robust, unbiased and reproducible AI
•
1 like•225 views
Talk at GOTO Copenhagen 2019
Recommended
Recommended
More Related Content
Similar to Trusting machines with robust, unbiased and reproducible AI
Similar to Trusting machines with robust, unbiased and reproducible AI (20)
More from Margriet Groenendijk
More from Margriet Groenendijk (19)
Recently uploaded
Recently uploaded (20)
Trusting machines with robust, unbiased and reproducible AI
- 1. Trusting machines with robust, unbiased and reproducible AI — Dr. Margriet Groenendijk Data & AI Developer Advocate
- 3. Deep Learning – Imaginary celebrities @MargrietGr https://arxiv.org/abs/1710.10196
- 4. @MargrietGr
- 5. @MargrietGr
- 8. "AI is the state of the art of computers, but calling it intelligence bothers me” @stevewoz #GOTOcph @MargrietGr
- 9. Machine learning Algorithm selection Deep learning Neural network design Artificial intelligence Systems architecture @MargrietGr
- 10. AI is used in many high-stakes decision making applications Credit Employment Admission HealthcareSentencing @MargrietGr
- 12. Gender Shades Project Released February 2018 @MargrietGr
- 16. 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
- 17. 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
- 18. @MargrietGr
- 20. 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/
- 24. Adversarial threats to AI Evasion attacks Performed at test time Perturb inputs with noise Model fails to predict correctly Undetectable by humans
- 25. Adversarial threats to AI Poisoning attacks Performed at training time Insert poisoned sample in training data Use backdoor later
- 26. ART Adversarial Robustness 360 Toolbox ↳ (ART) https://github.com/IBM/adversarial-robustness-toolbox 28 Toolbox Evasion attacks Defense methods Detection methods Robustness metrics https://art-demo.mybluemix.net/
- 27. 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
- 28. 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
- 29. AIF360 AI Fairness 360 ↳ (AIF360) https://github.com/IBM/AIF360 31 Toolbox Fairness metrics (70+) Fairness metric explanations Bias mitigation algorithms (10+) http://aif360.mybluemix.net/
- 35. 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
- 36. AIX360 AI Explainability 360 ↳ (AIX360) https://github.com/IBM/AIX360 Toolbox Local post-hoc Global post-hoc Directly interpretable http://aix360.mybluemix.net @MargrietGr
- 37. 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
- 38. 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
- 39. 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
- 40. @MargrietGr
- 46. 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
- 47. 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? @MargrietGr
- 49. @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
- 50. @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
- 51. @MargrietGr
- 52. @MargrietGr
- 53. @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: ibm.biz/BdzLdz
- 54. Provision services Watson Studio Cloud Object Store Watson Machine Learning Watson OpenScale @MargrietGr
- 55. Provision services Setup a project Watson Studio Jupyter notebooks @MargrietGr
- 56. Provision services Setup a project Deploy pre-trained model Watson Studio Jupyter notebooks SparkML model Deploy as API Test API @MargrietGr
- 57. 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
- 58. @MargrietGr
- 59. @MargrietGr
- 60. @MargrietGr
- 61. @MargrietGr
- 62. @MargrietGr
- 63. @MargrietGr
- 64. @MargrietGr
- 67. Provision services Setup a project Deploy pre-trained model Configure monitoring Use in production @MargrietGr
- 69. 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 cool It is state of the art It is exciting to build new applications It is not magic It is not intelligent
- 70. 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