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How businesses can build fairness into their machine learning models

Enterprise-wide deployments of AI are constrained by the requirements of scaling any new system or technology: transparency, security, and the application's ability to work across many systems. But solving for these challenges is not enough. Every organization that develops or uses AI, or hosts or processes data, must do so in ways that allow them to rationalize the decisions or recommendations in a way that is easily consumable. Much like an impressionable child, new technologies like AI are prone to influence by the nature of the information and data sets with which they are presented. Or, AI models could be unknowingly fed biased data that affects their output.