In this section, you learned about the critical aspects of maintaining and ensuring the effectiveness
of AI systems.
Post-deployment, continuous monitoring and validation were emphasized as essential practices to detect
and mitigate issues in real time, ensuring AI systems remain reliable and accurate.
You explored post-hoc testing methods to evaluate AI system performance, which involve retrospective
analyses to understand and enhance system accuracy and effectiveness.
The lesson on managing automation bias highlighted strategies to identify and reduce biases that can
emerge in automated decision making processes, maintaining fairness and ethical standards.
Additionally, best practices for model versioning and updates were discussed, focusing on systematic
approaches to track changes, manage versions, and implement updates without disrupting system operations.
Managing third party risks.
Post-deployment was covered, emphasizing the importance of monitoring and assessing third party components
to ensure they meet security and performance standards.
Reducing unintended use and downstream harm was explored, providing strategies to anticipate and mitigate
potential misuse of AI systems, thereby protecting users and stakeholders from adverse outcomes.
Finally, planning for AI system deactivation and system sunset was addressed, underscoring the necessity
of having structured plans for the end of life phase of AI systems to ensure a smooth and responsible
transition.
Through these lessons, you gained a comprehensive understanding of the ongoing responsibilities involved
in AI system maintenance, the importance of proactive and reactive measures to uphold system integrity,
and the ethical considerations that guide the responsible deployment of AI technologies.
This knowledge equips you to effectively manage AI systems throughout their life cycle, ensuring they
deliver value while minimizing risks and unintended consequences.