What approach does Watson take toward bias in AI models?

Watson adopts a proactive approach with fairness guidelines to address bias in AI models. This means that measures and considerations for reducing bias are integrated into the development process from the very beginning, rather than being applied after the models are deployed. By establishing fairness guidelines, Watson emphasizes the importance of ensuring equitable treatment and outcomes for all users and use cases from the outset. This proactive methodology involves continuous monitoring, evaluation, and adjustment of AI models throughout their lifecycle, allowing for the identification and mitigation of potential biases before they can cause harm or skew results.

In this context, a reactive approach might involve addressing issues only after they arise, which can lead to a greater risk of perpetuating existing biases. A manual review process after deployment alone is not sufficient, as it may miss issues that could have been identified and corrected during the design and training phases. While a strict no-tolerance policy sounds appealing, in practice, it may not be feasible to completely eliminate biases without an effective framework to actively manage and reduce them throughout the model development cycle. Thus, the proactive approach with established fairness guidelines is the most effective strategy for mitigating bias in AI models.