Artificial intelligence (AI) systems can exhibit bias due to a variety of factors. One source of bias in AI systems is biased data, which can be caused by historical or current discrimination against certain groups in society. For example, in 2018, Amazon's AI recruiting system was found to have a bias against women due to the fact that it was trained on a dataset that primarily consisted of male resumes [1].
In addition to biased data, AI systems can also be biased due to the inherent biases of their creators or users. For example, a study conducted by the World Economic Forum found that AI systems are more likely to replicate and amplify the biases of their creators and users, leading to discrimination against certain groups [2].
Another factor contributing to bias in AI systems is the use of biased algorithms. Algorithms can be biased due to the biases of their creators or due to the biases present in the data used to train them. For example, the COMPAS system, which is used by US courts to assess the likelihood of a defendant becoming a recidivist, was found to have a higher false positive rate for African Americans compared to Caucasians [10].
To address bias in AI systems, it is important to consider the biases that may be present in the data used to train the systems, as well as the biases of the creators and users of the systems. It is also important to ensure that AI systems are tested and evaluated for bias before they are deployed in real-world situations. There are also ongoing efforts to develop methods for detecting and mitigating bias in AI systems, including the use of fairness metrics and de-biasing algorithms [3, 4].
Overall, it is important to recognize that AI systems can be biased due to a variety of factors, and it is necessary to actively work to identify and address these biases in order to ensure that AI systems are fair and unbiased in their decision-making.
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