Deep learning fast and slow, a responsible and explainable AI framework - Ahmad Haj Mosa
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PwC's recently released Responsible AI Diagnostic surveyed around 250 senior business executives from May to June 2019. The survey says that 84% of CEOs agree that AI-based decisions need to be explainable in order to be trusted. In the past few years, Deep learning has shown remarkable results in various applications, which makes it one of the first choices for many AI use cases. However, deep learning models are hard to explain, and since the majority of CEOs expect AI solutions to be explainable, deep learning has a serious challenge. Daniel Kahneman, in his book thinking fast and slow, presented two different systems the human brain uses to form thoughts and decisions: System 1: fast, intuitive and hard to explain System 2: slow, conscious and easy to explain In this talk I will present: A) PwC Responsible AI Survey B) A proposed deep learning framework that mimics the two systems of thinking C) The recent advances in the neural symbolic learning field.
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Deep learning fast and slow, a responsible and explainable AI framework - Ahmad Haj Mosa
- 2. Deep Learning Fast and Slow, A Responsible and Explainable AI Framework Presentation by Ahmad Haj Mosa Head of AI
- 3. PwC Ćsterreich PwC Global CEOs Survey 84%of CEOs agree that AI-based decisions need to be explainable in order to be trusted 3 Control Security Ethical Economic Performance ā¢ Risk of errors ā¢ Risk of bias ā¢ Risk of opaqueness ā¢ Risk of performance instability ā¢ Lack of human agency in AI supported processes ā¢ Inability to detect/control rogue AI ā¢ Adversarial attacks ā¢ Cyber intrusion risks ā¢ Privacy risks ā¢ Open source software risks ā¢ Lack of values risk ā¢ Value alignment risk ā¢ Reputational risk ā¢ Autonomous weapons proliferation ā¢ Risk of intelligence divide Risks ā¢ Job displacement ā¢ Liability risk ā¢ Risk of āwinner takes allā concentration of power Risks
- 4. PwC Ćsterreich Governments are actively developing legal frameworks for holding algorithms accountable Algorithmic Accountability Act (US) Companies are legally required to assess automated systems based on training data, model, fairness performance, bias, discrimination, privacy and security. Companies must conduct impact evaluation and rectify any identified issues. General Data Protection Regulation (EU) Companies should use personal information secured in a manner that prevents discriminatory effects. Appropriate mathematical procedures should be adopted for consumer profiling. The data subject possess the Right To Explanation to seek meaningful information about the logic involved in modeling California Consumer Privacy Act Modeled off GDPR in many ways, such as limiting data usage and requiring right to erasure. At this moment, CCPA does not contain provisions for the right to object to automated decision making. Globally, countries are exploring ethical AI standards. Recently, 42 countries signed on to the OECD Principles on Artificial Intelligence. Government Task Forces Globally, governments are also launching task forces and other exploratory bodies on AI and AI governance. Bodies include: - UK All Parliamentary Group on AI (Jan 2017) - NYC AI Task Force (May 2018) - Victoria All-Party Parliamentary Group on AI (March 2018)
- 9. What is XAI?
- 10. PwC Ćsterreich Our minds contain processes that enable us to solve problems we consider difficult. "Intelligence" is our name of those processes we don't yet understand. Marvin Minsky 10 November 2019
- 11. PwC Ćsterreich Our model contain processes that enable us to solve problems we consider difficult. āBlack-Box" is our name of those processes we don't yet understand. Marvin Minsky 11 November 2019
- 12. PwC Ćsterreich What is Explainable AI 12 November 2019 Why?How? High abstract explanations that are perceptive by users (non data scientists) Explanation of the mathematical models and how data flow from inputs to decisions
- 13. PwC Ćsterreich Explainability Types 13 Task Data Task Model Interpretation Scientist Scientist Data Task Model Explanation User Scientist Data Task Model Explanation User Scientist Interpretation Explanation Self Learned Explanation
- 14. PwC Ćsterreich Explanation 14 Explanation on Features Level Reasoning/Conceptual based Explanation Perceptive by Humans Possible integration in self-learned explanations 1 2 3 4 LIME Saliency Maps Shap TCAV Strong Average WeakStrong Average WeakStrong Average WeakStrong Average Weak
- 16. PwC Ćsterreich 1. Thinking Fast and Slow
- 17. PwC Ćsterreich 2. A Framework of Representing Knowledge source: Marvin Minsky: The society of mind
- 18. PwC Ćsterreich 3. The Consciousness Prior 1. High dimensional concepts are represented in the encoder h 2. Low dimensional consciousness thoughts/states are represented in c Input x unconscious state h conscious state c Attentions source: Yoshua Benjio: The Consciousness Prior
- 19. PwC Ćsterreich 4. Concept Activation Vector Domain Expert Task Scientist Model High Concepts Explanation š£ š¶
- 20. PwC Ćsterreich 4. Concept Activation Vector Domain Expert Task Scientist Model High Concepts Explanation š£ š¶ š š¶,š,š1 = šš( ) šš£ š¶ š š¶,š,š š = šš( ) šš£ š¶
- 21. PwC Ćsterreich Self learned conceptual explanations Logistic Classifier: concept Dog face Logistic Classifier: concept Huskey face Logistic Classifier: concept GrassLogistic Classifier: concept sand Positive Concept Negative Concept Input x unconscious state h conscious state c Attentions The Consciousness Prior Framework of Representing Knowledge
- 22. PwC Ćsterreich Self learned conceptual explanations Domain Expert Task Scientist Model High Concepts Explanation Domain Expert Task Scientist Model High Concepts Explanation Logistic Classifier: concept Dog face Logistic Classifier: concept Huskey face Logistic Classifier: concept GrassLogistic Classifier: concept sand Positive Concept Negative Concept High Level Reasoining
- 23. PwC Ćsterreich Self learned conceptual explanations Model Building Process: 1. Domain Expert defines a task: Criminals face detection 2. Domain Expert defines positive and negative concepts: ā Positive concepts: eyes, nose or hair ā Negative concepts: background or face color 3. Scientist uses/prepares a training data for the task, and a query engine for the concepts 4. Training the model: ā Backpropoagation from the task targets (cross-entropy for the logits) for some iterations ā Query images (google search?) that represents the Positive and Negative concepts ā Apply TCAV ā Use Reinforce techniques to force the representation to use positive concepts and to refuse negative concepts ā Repeat until convergence Domain Expert Task Scientist Model High Concepts Explanation
- 24. PwC Ćsterreich Use Case Anomaly detection in VAT data November 2019 24 ā¢ Detection of possible faults ā¢ Reduces the risk ā¢ Increases data quality Trading Company Inc. VAT Regulation
- 25. PwC Ćsterreich Anomaly Detection November 2019 25 Document Type: Posting Date: Posting Time: Posting Key: General-Ledger: Amount: Vendor: . . . Transaction-Code: ā¢ RE ā¢ 06.11.2019 ā¢ 09:12 ā¢ 31 ā¢ 00404002 ā¢ 8.350,12 ā¢ John Doe Ltd. . . . ā¢ TC 043 ā¢ RE ā¢ 06.11.2019 ā¢ 09:12 ā¢ 21 ā¢ 00404002 ā¢ 8.350,12 ā¢ John Doe Inc. . . . ā¢ TC 043 š„1 š š„2 š š„3 š š„4 š š„5 š ā® š„2 š Feature1Featurek . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . š„1 š š„2 š š„3 š š„4 š š„5 š ā® š„2 š Feature2FeaturekFeature1 Feature2 Journal Entry Attributes š Input Journal Entry šš Reconstructed Journal Entry šš Decoder š š³Encoder š š³ āLatent Spaceā Neurons š = (š š, š š, ā¦ , š š) ļ¼ ļ¼ ļ¼ ļ¼ ļ¼ ļ¼ ļ» ļ» ļ¼ Korrekte Rekonstruktion ļ» Inkorrekte Rekonstruktion
- 26. PwC Ćsterreich Explainable Anomaly Detection
- 27. PwC Ćsterreich Summary Why? ā¢ For Business, Risk, Ethics and Law Explainable AI is a key feature to consider in your development ā¢ 84% of global CEOs think Explainable AI is important When? ā¢ When the task an AI is automating usually requires explanation from humans ā¢ When the legal, life or cost risk of AI decisions is high How? ā¢ If the explanation is only needed when things go wrong (Self driving car accident) then interpretation or post training explanation from the developer is enough ā¢ If the explanation is needed at every decision (medical diagnosis) then self-learned explanation is important Something to take ā¢ Neural Symbolic AI ā¢ Consensuses Priors ā¢ Concept Vectors Activations 27 November 2019
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