Walker releases two new books on faith walk
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This article reviews the existing FDA programs impacting artificial intelligence (AI) / machine learning (ML) technologies in medtech, as well as the...
Bias in AI can result from assumptions in the machine learning process, or as a result of data that is imbalanced or incomplete and does not reflect a true representation of the relevant population. Examples of such skewed data include datasets that are mislabeled or misrepresentative of reality, systematic errors in the collection of data, or valuable data that is completely excluded, which in turn creates biased outputs. The implications of bias in AI are widespread, and can affect recruiting processes, credit referencing, and insurance decisions, to name a few. For example, if an employer ...