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7.6 Artificial Intelligence (AI)

7.6A AI - Introduction 

  Class Outline 
    Intelligence and Emotion
    Deep Learning, Machine Learning, and AI
    Autonomous Systems

  Class Description (by Jaeseung Jeong)

  Lecture Candidates: Jaeseung Jeong, Joanna Bryson/Bath

7.6B AI Governance 

  Class Outline (Draft)

     AI Principles; Asilomar Principles, Asimov's Law,
     Impact on Labor Market

  Class Description (by Danit Gal)

  This lecture on AI governance will be divided into three sections. The first will provide an overview of the ethical, inclusive, and societal aspects of AI. We will examine key documents such as the IEEE’s        Ethically Aligned Design v.2, the Asilomar Principles, and the AI Now report. The lecturer will also offer first-hand insights from relevant conferences on these topics. The second will discuss the economic and regulatory aspects of AI. We will examine government regulations, policies, and standards from around the world and key documents such as the AI Index, reports from global firms, and other relevant documents. The third will examine the technical aspects of AI governance, including safety, robustness, and security. We will examine outputs and insights from leading institutions and thinkers in the field, and review existing best practices in the field. Each section will provide a brief overview of existing materials to inform participants of ongoing discourses in these fields, and offer questions for discussion. The following discussion will be themed around the relevance and application of these global discourses to Asia, with an emphasis on East and Southeast Asia.

 Class Description (by Sunyoung Yang)

   Applicability of Existing Global Governance Models for the Formation of Global AI Governance 

   Recent innovations in Artificial Intelligence (AI) have been accelerated by computing technologies and big data. AI can be defined as “activity devoted to making machines intelligent, and intelligence is               that quality that enables an entity to function appropriately and with foresight in its environment” (Nilsson 2010). AI is both creating new frontiers for social experience, public infrastructure, and industrial               innovation, but it also threatens to accelerate automation, social alienation, and global inequities. The impact of AI has worldwide implications, which calls for globally collaborative efforts to develop a joint          governance of AI, as well as the need to allow diverse groups to contribute to the process. New regulations for self-driving cars and aircrafts, imposition of taxes for robot labor, and banning of autonomous          weapons are examples where AI governance is needed. This lecture intends to articulate the components required to develop a global governance of AI based on the model of multi-stakeholder processes        (Hemmati 2002). This lecture will determine what factors will be pertinent to AI governance from existing examples of global governance, including Internet Governance, the Intergovernmental Panel on                Climate Change, more broadly the UN Environment, and global governance of nuclear technology. This lecture will then synthesize the existing examples of global governance and analyze the applicability        of those examples for AI governance, examining Internet governance and AI governance within the broader context of digital governance.

 Thematic Pillars (by Partnership of AI)

        Safety-critical AI

        Fair, Transparent and Accountable AI

        Collaboration between People and AI

        AI, Labor and Economy

        Social and Societal Influences of AI

        AI and Social Good

  Lecture Candidates: Jaeseung Jeong, Sunyoung Yang, Danit Gal,...

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