Regularly review system algorithms and ensure diverse training datasets to minimize potential biases.
Enhance system performance monitoring and include stakeholders in regular evaluations of data handling and model outputs.
Conduct independent audits, implement robust bias detection mechanisms, and prioritize equitable outcomes in system design.
Comprehensive feedback mechanisms to address possible inaccuracies and biases within the data, thereby reducing disparities in system accuracy, including tools for assessing data quality, algorithms to detect biases, transparent reporting on data collection and processing, strategies for bias mitigation, continuous monitoring of system performance, education and training on bias awareness, user feedback channels, and ethical oversight in order to effectively navigate potential biases in the data and enhance the fairness and accuracy of the recommender system, ultimately improving patient care and outcomes.
Harmonizing technology & Optimizing people’s health experience