Use large, representative data sets It's critical to ensure that your data sets are robust and include real-world situations. Synthetic data sets help fill data gaps, but companies must ensure that they actively create diverse and realistic data sets when creating AI models. Testing AI and machine learning models is one way to avoid bias before making algorithms known. Responsible and successful companies must know how to reduce biases in AI and, to do so, proactively use their training data.
To minimize bias, control outliers by applying statistics and exploring data. At a basic level, AI bias is reduced and avoided by comparing and validating different samples of training data to verify their representativeness. Without this bias management, any AI initiative will eventually fail. Wright noted that the European Union's Artificial Intelligence Act would change the rules of the game in an effort to remove biases from technology.
As has happened with previous waves, these technologies reduce the need for human labor, but they pose new ethical challenges, especially for artificial intelligence development companies and their customers.
