A sudden shift in the incoming data distribution-such as seasonal changes in customer behavior-is known as data drift. CPMAI defines model drift as "degradation in a model's performance over time as the underlying data distribution changes," implying that the root cause is the data itself shifting. Recognizing data drift is the first step in adapting both data pipelines and models to maintain performance .
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CPMAI's Domain VI: Trustworthy AI is designed to span the entire project lifecycle, embedding ethics, transparency, fairness, privacy, and security checks into every phase-from Business Understanding through Operationalization-so that trustworthy practices are not an afterthought but a continuous, integrated activity .
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This reaction is a classic example of the Uncanny Valley phenomenon, where a nearly human-like robot triggers discomfort or eeriness in users because it sits in the valley between clearly robotic and convincingly human appearances. Although not explicitly named in the CPMAI glossary, addressing this user experience concern falls under Continuous Improvement and Respect for People, ensuring cognitive solutions are designed for positive user acceptance.
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When training data under-represents or skews certain groups, the resulting model can systematically discriminate against those groups-a phenomenon termed algorithmic discrimination in the CPMAI Glossary.
Such bias in outcomes arises directly from biased training data.
Moreover, the CPMAI Exam Content Outline emphasizes that Trustworthy AI must apply laws pertaining to AI ethics, bias, and fairness to detect and remediate these issues early in the lifecycle.
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Generative AI encompasses techniques that create new data based on learned patterns. Outpainting is the specific generative approach for extending an image's content beyond its original borders. It leverages the same learned representations as inpainting or super#resolution but focuses on generating entirely new regions around an existing image.