Enabling Artificial Intelligence Adoption through Assurance

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