[AISWorld] AI and Big Data Analytics Management, Governance and Compliance: HICSS 53 Final CFP

[Michael Goul]

Final Call for papers - AI and Big Data Analytics Management, Governance and Compliance
    
HICSS-53: January 7-10, 2020 | Grand Wailea, Maui (Deadline for submissions: June 15, 2019)
    
    We seek submissions of original work addressing challenges, theoretical lenses, frameworks, development, evaluation, and impact studies assessing the implications of Artificial Intelligence and Big Data Analytics management, governance, and compliance. We also encourage submissions of research-in-progress as well as those that are practically oriented yet have the potential to make significant contributions to the research community.
    
    Relevant topics for the minitrack include, but are not limited to, the following:
    ·      Innovative AI, Big Data, data science and analytics governance approaches
    ·      Chief AI Officer, Chief Data Officer and Chief Analytics Officer roles and responsibilities
    ·      Data, analytical model and algorithm asset management
    ·      Analytics workflow management
    ·      Analytic model life-cycle management
    ·      Model management platform design
    ·      Model compliance management
    ·      Model documentation
    ·      Ethics of AI and data analytics
    ·      Analytics regulatory/compliance risks and risk mitigation
    ·      Data and model transparency
    ·      Business value of AI governance
    ·      Platform economics and strategy
    ·      Analytics documentation and metadata design
    ·      Legal implications of AI and data governance policies
    ·      Implications of legal analytics advances
    ·      Managing and deploying champion and challenger models
    ·      Campaign documentation and model reuse
    ·      Data and model ownership and contracts
    
    As background - Artificial Intelligence and Big Data applications are becoming increasingly important strategic assets as they enable organizations to differentiate themselves from their competitors by offering new intelligence and data-driven products and services and by achieving increased agility in operations and decision making.  Organizations are also discovering novel, innovative insights, and they are making decisions and acting upon them in a faster, more streamlined manner.  As organizations become more reliant on intelligent, data-driven models for insight, decision making and action, they need new theories, frameworks and methodologies that can help them:
    ·      Realize strategies and principles for managing Artificial Intelligence and Big Data
    ·      Streamline processes to develop and deploy analytical models and machine learning algorithms
    ·      Design new KPIs and deploy actionable dashboards
    ·      Manage and staff AI, ML and data science teams
    ·      Structure analytics functions/capabilities within organizations
    ·      Design, staff and provide direction to AI, data and analytics governance committees
    ·      Evaluate ethical implications of deployed analytical models and machine learning algorithms
    ·      Manage AI and Big Data Analytics project and deployment risk and
    ·      Advance AI and Big Data capability maturity
    
    Minitrack: AI and Big Data Analytics Management, Governance and Compliance
    Track:  Organizational Systems and Technology
    
    Minitrack Co-Chairs:
        Michael Goul (Primary Contact)
 - Arizona State University
 - Michael.Goul at asu.edu
        Jeffrey Saltz - Syracuse University - jsaltz at syr.edu
        Anna Sidorova - University of North Texas - anna.sidorova at unt.edu