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Enabling Artificial Intelligence Adoption through Assurance

Author

Listed:
  • Laura Freeman

    (Virginia Polytechnic Institute, State University (Virginia Tech), 900 N. Glebe Road, Arlington, VA 22203, USA)

  • Abdul Rahman

    (Virginia Polytechnic Institute, State University (Virginia Tech), 900 N. Glebe Road, Arlington, VA 22203, USA)

  • Feras A. Batarseh

    (Virginia Polytechnic Institute, State University (Virginia Tech), 900 N. Glebe Road, Arlington, VA 22203, USA)

Abstract

The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent systems. AI assurance provides the necessary tools to enable AI adoption into applications, software, hardware, and complex systems. AI assurance involves quantifying capabilities and associating risks across deployments including: data quality to include inherent biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. Data, algorithmic, and context/domain-specific factors may change over time and impact the ability of AI systems in delivering accurate outcomes. In this paper, we discuss the importance and different angles of AI assurance, and present a general framework that addresses its challenges.

Suggested Citation

  • Laura Freeman & Abdul Rahman & Feras A. Batarseh, 2021. "Enabling Artificial Intelligence Adoption through Assurance," Social Sciences, MDPI, vol. 10(9), pages 1-15, August.
    • Handle: RePEc:gam:jscscx:v:10:y:2021:i:9:p:322-:d:621156
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    References listed on IDEAS

    as
    1. Scott Emmons & Stephen Kobourov & Mike Gallant & Katy Börner, 2016. "Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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