Enabling Artificial Intelligence Adoption through Assurance

Laura Freeman, Abdul Rahman and Feras A. Batarseh
Additional contact information
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

Social Sciences, 2021, vol. 10, issue 9, 1-15

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.

Keywords: AI assurance; data quality; operating envelopes; validation and verification; XAI; AI trustworthiness; data democracy (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2076-0760/10/9/322/pdf (application/pdf)
https://www.mdpi.com/2076-0760/10/9/322/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jscscx:v:10:y:2021:i:9:p:322-:d:621156

Access Statistics for this article

Social Sciences is currently edited by Ms. Yvonne Chu

More articles in Social Sciences from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().