XMANAI - Foundations for Explainable AI
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This document summarizes a workshop on Explainable AI in Manufacturing. It discusses the XMANAI project, which received EU funding to develop explainable AI techniques for manufacturing. The project involves 15 partners across 7 countries working on 4 demonstrator applications. It aims to increase trust and transparency in AI by developing techniques to explain AI model decisions and predictions. The document outlines the project's approach, including developing an XAI platform and models catalogue, applying XAI to manufacturing use cases, and collaborating with stakeholders to design and evaluate explainable AI pipelines.
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XMANAI - Foundations for Explainable AI
- 1. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957362 XMANAI Foundations for Explainable AI “Explainable AI in Manufacturing” Workshop, October 11th, 2021 Dr. Fenareti Lampathaki (Suite5) – XMANAI Technical Coordinator
- 2. TABLE OF CONTENTS Introduction Explainable AI, Scope 01 02 03 Approach Concept, Workflows, Business Cases Conclusions Key Take-Aways, Next Steps “Explainable AI in Manufacturing” Workshop, Online 2
- 3. Introduction 01 Explainable AI, Scope 3 “Explainable AI in Manufacturing” Workshop, Online
- 4. • GA Number: 957362 • Title: Explainable Manufacturing Artificial Intelligence • Start Date: 1/11/2020 • End Date: 30/04/2024 • Overall Budget: 5.998.902 • Coordinator: TXT e-solutions SpA (Italy) • Funding Scheme: RIA - Research and Innovation Action • Topic: ICT-38-2020 - Artificial intelligence for manufacturing 4 “Explainable AI in Manufacturing” Workshop, Online XMANAI at a Glance
- 5. INNO/UNIMETRIK 5 “Explainable AI in Manufacturing” Workshop, Online XMANAI Consortium • 15 partners • 7 Countries • 4 demonstrators TXT/POLIMI/WHIRLPOOL/CNH Fraunhofer TYRIS AIDEAS SUITE5 ATHENA/UBITECH KBIZ DBL FORD
- 6. Why is Explainable AI important? ❑ “Why did the AI system make a specific prediction or decision?” ❑ “Why didn’t the AI system decide something else?“ ❑ “When did the AI system succeed and when did it fail and what was the impact?” ❑ How to avoid undetected bias, mistakes, and miscomprehensions creeping into decision-making? ❑ How to ensure fair decision making without compromising security and privacy? ❑ How to facilitate robustness, accuracy and performance without creating additional liabilities? ❑ How to provide really actionable insights? I. Increase Human Trust in AI II. Increase Transparency and Reliability of AI If humans do not understand why/how a decision/prediction is reached, they shall not adopt/enforce it…
- 7. 7 “Explainable AI in Manufacturing” Workshop, Online “Data / XAI” Challenges II. Efficient and Secure Data Management I. Traceable XAI Pipeline Lifecycle Management III. Trusted Data and XAI Model Sharing ❑ Properly ingesting, understanding, preparing and manipulating the data ❑ Handling inconsistent, incomplete or missing data with low dimensionality -> “small data” to handle overfitting or underfitting XAI models ❑ Managing data drift at XAI pipelines “operation” ❑ Data security - data always “on-premise” restrictions ❑ Collaboratively creating, training, validating, applying and re-training XAI models within XAI pipelines ❑ Managing XAI models’ explanations at different phases/levels, customized per stakeholder ❑ Packaging, deploying and scaling XAI models/pipelines in different execution environments in an interoperable manner (AI-Ops) ❑ Keeping track of XAI experiments and reproducing code and results ❑ Collaboration between data scientists, engineers and business experts ❑ IPR-compliant “assets” exchange ❑ XAI’s “transparency paradox” (XAI models security) & Ethics compliance
- 8. Approach 02 Concept, Processes 8 “Explainable AI in Manufacturing” Workshop, Online
- 9. Explainable AI Circles “Explainable AI in Manufacturing” Workshop, Online 9 AI Axis I. Basic Analytics AI Axis II. Machine Learning AI Axis III. Deep Learning Intrinsically Interpretable AI Models - Understand AI Models - Hybrid AI Models - Understand AI Results - Traditional AI Models – Understand Data - Trust Level 1 - Emerging XAI Circle Trust Level 2 - Developing XAI Circle Trust Level 3 - Established XAI Circle Sample Data Exploration Structure & Semantics Visualizations Elicited Explanations Features Surrogate Models Directly Interpretable Models Explanation Interfaces Knowledge Graphs -> Graph Feature Engineering -> Graph Native Learning A Catalogue of (Baseline & Trained) Explainable AI models available through the XMANAI Platform: ✓ Directly interpretable vs. Post-hoc explanation ✓ Local vs. Global ✓ Static vs. Interactive
- 10. 10 “Explainable AI in Manufacturing” Workshop, Online Explainable AI through Stakeholders’ Collaboration Collaboration over XAI Assets including: Datasets, Baseline/Trained XAI models, XAI Pipelines, Features, Experiments, Explanations
- 11. XAI Journey though the “Business Expert” Perspective “Explainable AI in Manufacturing” Workshop, Online 11 ❑ Phase 1: AI Preparation - Provide and Understand Data ❑ Phase 2: AI Experimentation - Contribute to better understanding and evaluating the AI models / pipelines / results ❑ Phase 3: AI Insights - Understand AI results
- 12. XAI Journey though the “Data Scientist” Perspective “Explainable AI in Manufacturing” Workshop, Online 12 ❑ Phase 1: AI Preparation - Understand the data and the problem at hand ❑ Phase 2: AI Design - Prepare the data and handle problematic data cases ❑ Phase 3: AI Design - Collaboratively Design AI pipelines ❑ Phase 4: AI Insights - Explain AI models / results ❑ Phase 5: AI Evaluation - Evaluate AI models / results
- 13. 13 “Explainable AI in Manufacturing” Workshop, Online XMANAI MVP View Explainability Features ❑ XAI Model Management ❑ XAI Pipeline Design ❑ Collaboration over XAI pipelines creation ❑ XAI Model Security Assessment
- 14. XMANAI Architecture: XAI Platform, Models Catalogue & Apps Core XAI Management Platform Open APIs Secure Execution Clusters (SEC) Process Optimization App XMANAI ON-PREMISE (PRIVATE CLOUD) ENVIRONMENTS Stakeholders’ On-Premise Environment / Installation XMANAI (CENTRALIZED) CLOUD INFRASTRUCTURE XMANAI MANUFACTURING APPS PORTFOLIO Product Demand Forecasting App Process/Product Quality Optimization App Process Optimization & Semi- Autonomous Planning App Services in a nutshell: • Data & Models Collection Services • Scalable Storage Services • Data Manipulation Services • XAI Model Lifecycle Services • XAI Pipeline Execution Services • XAI Insights Services • Secure Asset Sharing Services • Data & Models Governance Services “Explainable AI in Manufacturing” Workshop, Online
- 15. AI for Production Optimization AI for Product Demand Planning AI for Process/Product Quality Optimization AI for Smart Semi- autonomous Hybrid Measurement Planning 15 “Explainable AI in Manufacturing” Workshop, Online XAI Approach Application in 4 Manufacturing Demonstrators ❑ Anomaly Detection & Unwanted scenarios alert system ❑ Automated Production Planning ❑ Product Demand Forecasting ❑ Production Scheduling ❑ XAI-operator collaborative maintenance ❑ Point cloud optimization ❑ Measurement plan parameter optimization App 1 App 2 App 3 App 4
- 16. Conclusions 03 Key Take-Aways, Next Steps 16 “Explainable AI in Manufacturing” Workshop, Online
- 17. Project “Technical” Achievements in Year 1 “Explainable AI in Manufacturing” Workshop, Online 17 State-of-the Art Analysis Business Cases & Requirements Elicitation Technical Requirements & MVP Definition Asset Management Bundles Methods & Specifications AI Bundles Methods & Specifications Platform & Apps Architecture Design
- 18. • Collaboration among stakeholders in XAI needs to be appropriately cultivated. • XAI-focused platforms are still in their infancy • Next steps of our work include following the XAI approach proposed in: • Finalisation of the Platform and Apps Design • Population of the XAI models’ catalogue • Platform and Apps Development • Validation in the 4 manufacturing cases 18 “Explainable AI in Manufacturing” Workshop, Online Conclusions & Next Steps I. Without Data, there is no AI ! II. Without AI explainability, there is no trust ! III. Without AI trust & ethics, there is no adoption ! Explainability is not one-way path…
- 19. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957362 Thank your for your attention! 19 fenareti@suite5.eu
- 20. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957362 www.ai4manufacturing.eu info@xmanai.eu /XMANAI /XMANAI /XMANAI 20 Dr. Fenareti Lampathaki Technical Director Suite5 Data Intelligence Solutions Limited www.suite5.eu fenareti@suite5.eu /fenareti /fenareti.lampathaki / fenareti.lampathaki