Medical Robotics
Bias in algorithms refers to systematic errors that result from flawed assumptions in the machine learning process, leading to unfair or inaccurate outcomes. This bias can arise from various sources, including the data used for training, the design of the algorithm, and the decision-making processes that shape how data is interpreted. Understanding bias is crucial for ensuring that machine learning systems used in surgical task automation operate fairly and effectively, minimizing risks associated with erroneous predictions or decisions.
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