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Artificial Intelligence: Technology, Governance & Social Innovation

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Artificial Intelligence: Technology, Governance & Social Innovation PROCEEDINGS OF THE 2019 MMPA CONFERENCE ARTIFICIAL INTELLIGENCE: TECHNOLOGY, GOVERNANCE & SOCIAL INNOVATION Held on November 15, 2019 Institute for Management & Innovation Innovation Complex, Kaneff Centre University of Toronto Mississauga 3359 Mississauga Road DISCLAIMER Views and opinions expressed are those of the authors and not necessarily those of their organizations nor the University of Toronto. The University of Toronto and the authors do not accept any responsibility or liability that might occur directly or indirectly as a consequence of the use, application or reliance on this material. Table of Contents Table of Contents ................................................................................................................................. ii Foreword — CPA Canada ...................................................................................................................... 7 Michael Wong and Davinder Valeri ......................................................................................................... 7 Artificial Intelligence: Technology, Governance, and Social Innovation — an Introduction .................... 9 Irene M. Wiecek ....................................................................................................................................... 9 COVID-19 Update .......................................................................................................................... 11 TECHNOLOGY PILLAR .......................................................................................................................... 13 AI and Industrial Innovations .............................................................................................................. 13 Fakhri Karray .......................................................................................................................................... 13 Introduction ................................................................................................................................... 13 Global Industry Revolutions ........................................................................................................... 13 Relationships among AI, Machine Learning, and Big Data ............................................................ 16 Waterloo AI Institute ..................................................................................................................... 19 Foundational AI versus Operational AI: Research at Waterloo.ai ............................................. 20 How to Build Capacity in AI ............................................................................................................ 20 At the Micro (Company) Level ................................................................................................... 20 At the Macro (Country) Level .................................................................................................... 20 AI: The Positive Side ....................................................................................................................... 21 AI Challenges and Risks .................................................................................................................. 22 Conclusion ..................................................................................................................................... 24 Keynote Speech: Controlling the Black Box: Learning Manipulable and Fair Representations ............. 25 Richard Zemel ........................................................................................................................................ 25 Introduction ................................................................................................................................... 25 Progress in Machine Learning ........................................................................................................ 26 Deep Learning Defined: Supervised Deep Learning ...................................................................... 26 Developing Strong Representations: The Layer Below the Output ............................................... 27 Generative Models: Systems That Can Produce Inputs (Semi-Supervised Learning) ................ 28 Training the network .............................................................................................................. 29 ii Developing Fairness in Automated Decision Making .................................................................... 32 Fair Representations .................................................................................................................. 33 Flexibly Fair VAE (FFVAE) ........................................................................................................ 33 Building more robust classifiers: Invertible Deep Networks .......................................................... 34 Conclusion ..................................................................................................................................... 35 Current Research Directions ...................................................................................................... 36 Machine + MD: The Role of AI in Computer-Integrated Interventional Medicine ................................ 37 Parvin Mousavi ...................................................................................................................................... 37 Introduction: AI in Medicine .......................................................................................................... 37 Hypothesis-Driven to Data-Driven Science .................................................................................... 37 Machine Learning for Computer-Aided Interventions ................................................................... 39 Heterogeneity: The Challenge for AI in Medicine ...................................................................... 39 Innovation, Democratization and Data-Driven, Theory-Guided AI ................................................ 40 Example 1: Prostate Cancer Diagnosis and Intervention ........................................................... 41 Example 2: Heart Disease .......................................................................................................... 44 The G7: AI and Society ................................................................................................................... 47 The Canadian Perspective: Automation Not Domination .............................................................. 48 Conclusion ..................................................................................................................................... 50 GOVERNANCE PILLAR ......................................................................................................................... 51 AI and the Future of the Accounting Profession: Déjà Vu All Over Again ............................................. 51 Miklos Vasarhelyi ................................................................................................................................... 51 Introduction ................................................................................................................................... 51 New Skills and Competencies ........................................................................................................ 51 AI Evolution .................................................................................................................................... 52 In Accounting and Auditing ....................................................................................................... 53 Exogenous Data ............................................................................................................................. 54 No Rules for Use ........................................................................................................................ 54 Exogenous Data Analytics for Auditing ...................................................................................... 55 CarLab Audit Research ................................................................................................................... 56 Conclusion ..................................................................................................................................... 61 iii Computerization of Occupations ........................................................................................... 61 Risks of AI and Responsible AI ............................................................................................................ 63 Anand Rao .............................................................................................................................................. 63 Introduction ................................................................................................................................... 63 AI is closer than we imagine… ................................................................................................... 63 But AI is much farther off than we think… ................................................................................ 64 The Digital Revolution .................................................................................................................... 64 Two paths to AI .......................................................................................................................... 64 AI definition ..............................................................................................................................
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