How is fairness monitored in Watson's AI models?

Fairness in Watson's AI models is primarily monitored by implementing fairness guidelines and monitoring algorithms. This approach involves integrating specific metrics and frameworks during the model development and evaluation phases to assess how equitably the model performs across various demographic groups. This includes identifying and mitigating biases that may lead to unfair treatment of particular groups based on characteristics such as race, gender, or age.

Incorporating monitoring algorithms allows continuous assessment of the model's fairness throughout its lifecycle. By utilizing techniques like bias detection tools and transparency assessments, developers can actively manage and improve fairness, ensuring adherence to both ethical standards and regulatory requirements.

The other approaches, while potentially useful in certain contexts, do not directly address the need for systematic and ongoing assessment of fairness in AI models. Regular user surveys may help gather subjective feedback, but they lack the technical rigor needed for a comprehensive fairness evaluation. Automated code reviews focus more on code quality and best practices rather than fairness metrics. External audits provide valuable insights but are typically periodic and not as integrated into the continuous development process as monitoring algorithms are.