uncertainty

Terms from Artificial Intelligence: humans at the heart of algorithms

Page numbers are for draft copy at present; they will be replaced with correct numbers when final book is formatted. Chapter numbers are correct and will not change now.

Uncertainty in AI may arise from the data (e.g. inaccuracy of {[sensors}}) or due to the processing of the data (e.g. stochastic nature of some neural-network training methods). In uncertainty may affect both the training sets used to create machine learning or other forms of AI; and the data needed for decsion maing when the AI model is deployed. For example, if an image recognistion algorithm is trained using past radiographic images, some may have been misclassified by previous human processes (uncertaunty in training data), but also when deployed some scans may be poor quality due to patients miving during scanning. There are various methods used in AI for uncertain reasoning. In addiiton, it is important when designing the human processes and user interactions that unceertainty is adequately communicated and overall processes created that are {[robust}}.

Used in Chap. 3: pages 29, 32, 36; Chap. 7: page 108; Chap. 11: pages 159, 170; Chap. 15: page 247; Chap. 18: pages 296, 302, 308, 310; Chap. 19: pages 313, 314, 318, 324, 327, 329