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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. Published version |
