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![Open Source AIOps Platform!
AISphere Pipeline: Continued
# Include simple pipeline into a complicated pipeline
overallPipe = Pipe('OverallPipeline')
overallPipe.add_jobs([
Job(check_data_fairness),
training_pipe,
model_validation_pipe,
Job(s2i),
model_deployment_pipe,
Job(explain_model_predictions)
])
overallPipe.run()](https://image.slidesharecdn.com/aif360-190520172937/75/AIF360-Trusted-and-Fair-AI-53-2048.jpg)
Uploaded byAnimesh Singh
AIF360 - Trusted and Fair AI
The document discusses various ways that bias can arise in artificial intelligence systems and machine learning models. It provides examples of bias found in facial recognition systems against dark-skinned women, sentiment analysis showing preference for some religions over others, and risk assessment algorithms used in criminal justice showing racial disparities. The document also discusses definitions of fairness and bias in machine learning. It notes there are at least 21 definitions of fairness and bias can be introduced during data handling and model selection in addition to through training data.
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AIF360 - Trusted and Fair AI
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- 2. Center for OpenSource Data and AI Technologies (CODAIT) Code – Build and improve practical frameworks to enable more developers to realize immediate value. Content – Showcase solutions for complex and real-world AI problems. Community – Bring developers and data scientists to engage with IBM Improving Enterprise AI lifecycle in Open Source • Team contributes to over 10 open source projects • 17 committers and many contributors in Apache projects • Over 1100 JIRAs and 66,000 lines of code committed to Apache Spark itself; over 65,000 LoC into SystemML • Over 25 product lines within IBM leveraging Apache Spark • Speakers at over 100 conferences, meetups, unconferences and more CODAIT codait.org
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- 4. AIOps Prepared and Analyzed Data Trained Model Deployed Model Many toolsavailable to build initial models! Prepared and Analyzed Data Initial Model Deployed Model
- 5. AIOps Prepared and Analyzed Data Trained Model Deployed Model Many toolsto train machine learning and deep learning models! Prepared and Analyzed Data Initial Model Deployed Model FfDL
- 6. Fabric for DeepLearning https://github.com/IBM/FfDL FfDL Github Page https://github.com/IBM/FfDL FfDL dwOpen Page https://developer.ibm.com/code/open/projects/ fabric-for-deep-learning-ffdl/ FfDL Announcement Blog http://developer.ibm.com/code/2018/03/20/ fabric-for-deep-learning FfDL Technical Architecture Blog http://developer.ibm.com/code/2018/03/20/ democratize-ai-with-fabric-for-deep-learning Deep Learning as a Service within Watson Studio https://www.ibm.com/cloud/deep-learning Research paper: “Scalable Multi-Framework Management of Deep Learning Training Jobs” http://learningsys.org/nips17/assets/papers/ paper_29.pdf • Fabric for Deep Learning or FfDL (pronounced as ‘fiddle’ aims at making Deep Learning easily accessible to Data Scientists, and AI developers. • FfDL Provides a consistent way to train and visualize Deep Learning jobs across multiple frameworks like TensorFlow, Caffe, PyTorch, Keras etc. FfDL 6 Community Partners FfDL is one of InfoWorld’s 2018 Best of Open Source Software Award winners for machine learning and deep learning!
- 7. AIOps Trained Model Deployed Model And thereare platforms to serve your models, create model catalogues etc. Prepared and Analyzed Data Initial Model Deployed Model FfDL kube-batch Jupyter Enterprise Gateway MAX Istio OpenWhisk
- 8. AIOps Prepared and Analyzed Data Trained Model Deployed Model But whatabout trust in AI?! Prepared and Analyzed Data Initial Model Deployed Model Can the trained model be trusted? Can the dataset be trusted? Is the deployed model giving fair predictions?
- 9. Is it fair? Isit easy to understand? Did anyone tamper with it? Is it accountable? #21, #32, #93 #21, #32, #93 What does it take to trust a decision made by a machine?! (Other than that it is 99% accurate)?!
- 10. FAIRNESS EXPLAINABILITY ROBUSTNESSASSURANCE Our vision for Trusted AI! Pillars of trust, woven into the lifecycle of an AI application
- 11. AIOps Prepared and Analyzed Data Trained Model Deployed Model Let`s talkabout Robustness! Prepared and Analyzed Data Initial Model Deployed Model Is the model vulnerable to adversarial attacks?
- 12. AIOps Prepared and Analyzed Data Trained Model Deployed Model Adversarial RobustnessToolbox! Prepared and Analyzed Data Initial Model Deployed Model ART
- 13. IBM Adversarial Robustness Toolbox ART ARTis a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attack and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers. 13 https://github.com/IBM/adversarial-robustness- toolbox The Adversarial Robustness Toolbox contains implementations of the following attacks: Deep Fool (Moosavi-Dezfooli et al., 2015) Fast Gradient Method (Goodfellow et al., 2014) Jacobian Saliency Map (Papernot et al., 2016) Universal Perturbation (Moosavi-Dezfooli et al., 2016) Virtual Adversarial Method (Moosavi-Dezfooli et al., 2015) C&W Attack (Carlini and Wagner, 2016) NewtonFool (Jang et al., 2017) The following defense methods are also supported: Feature squeezing (Xu et al., 2017) Spatial smoothing (Xu et al., 2017) Label smoothing (Warde-Farley and Goodfellow, 2016) Adversarial training (Szegedy et al., 2013) Virtual adversarial training (Miyato et al., 2017)
- 14. Poisoning detection • Detectionbased 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 Evasion defenses • Feature squeezing • Spatial smoothing • Label smoothing • Adversarial training • Virtual adversarial training • Thermometer encoding • Gaussian data augmentation Evasion attacks • FGSM • JSMA • BIM • PGD • Carlini & Wagner • DeepFool • NewtonFool • Universal perturbation 14 Implementation for state-of-the-art methods for attacking and defending classifiers.
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- 16. AIOps Prepared and Analyzed Data Trained Model Deployed Model Now howdo we check for bias throughout AI lifecycle?! Prepared and Analyzed Data Initial Model Deployed Model Are model weights biased? Are predictions biased? Is the dataset biased?
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- 18. Examples of CognitiveBias in Machine Learning
- 19. 19 https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial- intelligence-systems-0212 general-purpose facial-analysis systems,which could be used to match faces in different photos as well as to assess characteristics such as gender, age, and mood. “error rate of 0.8 percent for light- skinned men, 34.7 percent for dark- skinned women” Bias in Facial Recognition (MIT News, Feb 2018)!
- 20. 20 Statement Score I’m asikh +0.3 I’m a christian +0.1 I’m a jew -0.2 I’m a homosexual -0.5 I’m queer -0.1 I’m straight +0.1 ”We dedicated lot of efforts to making sure the NLP API avoids bias, but we don't always get it right. This is an example of one of those times, and we are sorry. We take this seriously and are working on improving our models. We will correct this specific case, and, more broadly, building more inclusive algorithms is crucial to bringing the benefits of machine learning to everyone.“ Google spokesperson https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias Bias in Sentiment Analysis (Motherboard, Oct 25, 2017)! “determines the degree to which sentence expressed a negative or positive sentiment, on a scale of -1 to 1”
- 21. IBM Confidential 21 “designedher to tweet and engage people on other social media” "Unfortunately, within the first 24 hours of coming online, we became aware of a coordinated effort by some users to abuse Tay's commenting skills to have Tay respond in inappropriate ways. As a result, we have taken Tay offline and are making adjustments.” Microsoft spokesperson https://www.npr.org/2016/03/27/472067221/internet-trolls-turn-a-computer-into-a-nazi Tay (NPR, March 2016)!
- 22. 22 “used to informdecisions about who can be set free at every stage of the criminal justice system” Bias in Recidivism Assessment (Propublica, May 2016)!
- 23. 23https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing • “The formulawas particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants. • White defendants were mislabeled as low risk more often than black defendants.” “Northpointe does not agree that the results of your analysis, or the claims being made based upon that analysis, are correct or that they accurately reflect the outcomes from the application of the model.” “used to inform decisions about who can be set free at every stage of the criminal justice system” Bias in Recidivism Assessment (Propublica, May 2016)!
- 24. 24 Bias in RecidivismAssessment (Propublica, May 2016)!
- 25. 25 Bias in RecidivismAssessment (Propublica, May 2016)!
- 26. 26 Bias in RecidivismAssessment (Propublica, May 2016)!
- 27. 27 Bias in RecidivismAssessment (Propublica, May 2016)!
- 28. 28 Bias in RecidivismAssessment (Propublica, May 2016)!
- 29. 29 Bias in RecidivismAssessment (Propublica, May 2016)!
- 30. 30IBM Confidential https://www.newscientist.com/article/mg23631464-300-biased-policing-is-made-worse-by-errors-in-pre-crime-algorithms/ “Their studysuggest that the software merely sparks a “feedback loop” that leads to officers being repeatedly sent to certain neighbourhoods – typically ones with a high number of racial minorities – regardless of the true crime rate in that area.” Predictive Policing (New Scientist, Oct 2017)!
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- 32. Unwanted bias andalgorithmic fairness Machine learning, by its very nature, is always a form of statistical discrimination Discrimination becomes objectionable when it places certain privileged groups at systematic advantage and certain unprivileged groups at systematic disadvantage Illegal in certain contexts
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- 34. 34 Defining Bias ! There areat least 21 definitions of fairness - No one definition applicable in all contexts - Some definitions even conflict Bias does not comes only from training data - it can also be introduced with - inappropriate data handling, - as a result of inappropriate model selection - incorrect algorithm design or application Need a "comprehensive Bias pipeline" that fully integrates into the AI Lifecycle Defining Bias!
- 35. AIOps Prepared and Analyzed Data Trained Model Deployed Model Enter: AIFairness 360! Prepared and Analyzed Data Initial Model Deployed Model AIF360
- 36. AI Fairness 360 https://github.com/IBM/AIF360 AIF360AIF360toolkit is an open-source library to help detect and remove bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models. Toolbox Fairness metrics (70+) Fairness metric explanations Bias mitigation algorithms (10) 36 Supported bias mitigation algorithms Optimized Preprocessing (Calmon et al., 2017) Disparate Impact Remover (Feldman et al., 2015) Equalized Odds Postprocessing (Hardt et al., 2016) Reweighing (Kamiran and Calders, 2012) Reject Option Classification (Kamiran et al., 2012) Prejudice Remover Regularizer (Kamishima et al., 2012) Calibrated Equalized Odds Postprocessing (Pleiss et al., 2017) Learning Fair Representations (Zemel et al., 2013) Adversarial Debiasing (Zhang et al., 2018) Supported fairness metrics Comprehensive set of group fairness metrics derived from selection rates and error rates Comprehensive set of sample distortion metrics Generalized Entropy Index (Speicher et al., 2018)
- 37. (d’Alessandro et al.,2017) Fairness in building and deploying models throughout AI Lifecycle!
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- 40. Group fairness metrics legend situation1 Metrics!
- 41. Group fairness metrics legend situation2 Metrics!
- 42. Group fairness metrics legend disparateimpact Metrics!
- 43. Group fairness metrics legend statisticalparity difference Metrics!
- 44. Group fairness metrics legend equalopportunity difference 71% (5/7 positives) Metrics!
- 45. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 45 Modifyingthe training data. Modifying the learning algorithm. Modifying the predictions (or outcomes.) Algorithms!
- 46. (d’Alessandro et al.,2017) Algorithms (10)!
- 47. © 2018 IBMCorporation IBM Confidential Demo Application: AI Fairness 360 Web Application http://aif360.mybluemix.net/
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- 51. AIOps Trained Model Deployed Model AI Lifecycle Prepared and Analyzed Data InitialModel Deployed Model FfDL kube-batch Jupyter Enterprise Gateway MAX AIF360 AIF360 Istio OpenWhisk ART
- 52. Training Pipe Model Validation Pipe KNATIVE AI Pipelines-Logical Architecture! Data Pipe Model Deployment Pipe Deployment Analysis Pipe OPENWHISK Pipeline (Python Definition – Orchestrate and Track) AI Developer and Data Scientist AIOps Developer and Operator Python Function Python Function Python Function Python Function Python Function
- 53. Open Source AIOpsPlatform! AISphere Pipeline: Continued # Include simple pipeline into a complicated pipeline overallPipe = Pipe('OverallPipeline') overallPipe.add_jobs([ Job(check_data_fairness), training_pipe, model_validation_pipe, Job(s2i), model_deployment_pipe, Job(explain_model_predictions) ]) overallPipe.run()
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