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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 uses an AI tool for recruiting that uses historical data from the company’s past and current employees, most of whom are male, then the AI system may incorrectly “learn” that the ratio of preferable candidates should match this historical data, therefore resulting in a biased outcome. As the role of AI increases in decision making across industries, the risks of bias also increase due to t