What changes after AI rollout could affect its fairness over time?

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AI Fairness Can Be Affected by Post-Rollout Changes

Changes occurring after artificial intelligence systems are deployed in healthcare settings can impact their fairness over time. These include software updates, the introduction of new datasets, shifts in patient demographics, and the evolution of clinical practices. An AI system's performance, particularly its fairness across different patient groups, is not static and requires ongoing monitoring.
Software Updates and New Data Can Alter AI Performance

Artificial intelligence systems are not immutable once implemented; they can be altered through software updates or by incorporating new datasets. Such modifications can inadvertently influence the AI's decision-making processes, potentially affecting its accuracy and fairness. For instance, an update intended to improve a system's diagnostic capabilities might introduce unforeseen biases if not rigorously tested against diverse patient data representative of the local population.
Evolving Patient Populations and Clinical Practices Pose Challenges

As patient demographics shift or clinical practices evolve, an AI tool's initial performance metrics may no longer hold true. A system trained on patient data from one region, such as Europe or North America, may not perform equally well in Malaysia due to significant differences in patient characteristics, disease prevalence, and the way healthcare is delivered. For example, if a hospital's patient demographic changes to include a higher proportion of a specific ethnic group not well-represented in the original training data, the AI's fairness could be compromised.
Continuous Oversight is Crucial for Maintaining AI Reliability

Ensuring the long-term safety and reliability of AI in healthcare necessitates continuous oversight beyond initial regulatory approval. This ongoing monitoring is essential to detect and address any degradation in performance or emerging biases. Medical physicists, with their expertise in quality assurance and risk management, are identified as playing a critical role in this post-rollout phase. Their responsibilities are expanding from overseeing traditional medical equipment to evaluating the performance and fairness of AI algorithms.

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What changes after AI rollout could affect its fairness over time?

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