diff --git a/book/contents/core/privacy_security/privacy_security.qmd b/book/contents/core/privacy_security/privacy_security.qmd
index 44dc2718c..375e2970c 100644
--- a/book/contents/core/privacy_security/privacy_security.qmd
+++ b/book/contents/core/privacy_security/privacy_security.qmd
@@ -1560,3 +1560,12 @@ Importantly, security is not a static checklist. It is an evolving process shape
 The goal of this chapter was not to catalog every threat or prescribe a fixed set of solutions. Rather, it was to help build the mindset needed to design secure, private, and trustworthy ML systems—systems that perform reliably under pressure, protect the data they rely on, and respond gracefully when things go wrong.
 
 As we look ahead, security and privacy will remain intertwined with other system concerns: robustness, fairness, sustainability, and operational scale. In the chapters that follow, we will explore these additional dimensions and extend the foundation laid here toward the broader challenge of building ML systems that are not only performant, but responsible, reliable, and resilient by design.
+
+
+<!-- This is here to make sure that quizzes are inserted properly before a part begins. -->
+::: { .quiz-end }
+:::
+
+```{=latex}
+\part{key:responsibility}
+```
diff --git a/book/contents/core/robust_ai/robust_ai.qmd b/book/contents/core/robust_ai/robust_ai.qmd
index 91066c24d..f9272b32b 100644
--- a/book/contents/core/robust_ai/robust_ai.qmd
+++ b/book/contents/core/robust_ai/robust_ai.qmd
@@ -2687,11 +2687,3 @@ The chapter also addressed the impact of distribution shifts, which often result
 To navigate these risks, the use of robust tools and evaluation frameworks is essential. Tools such as PyTorchFI and Fidelity enable researchers and practitioners to simulate fault scenarios, assess vulnerabilities, and systematically improve system resilience. These resources are critical for translating theoretical robustness principles into operational safeguards.
 
 Ultimately, building robust AI requires a comprehensive and proactive approach. Fault tolerance, security mechanisms, and continuous monitoring must be embedded throughout the AI development lifecycle—from data collection and model training to deployment and maintenance. As this chapter has demonstrated, applying AI in real-world contexts means addressing these robustness challenges head-on to ensure that systems operate safely, reliably, and effectively in complex and evolving environments.
-
-<!-- This is here to make sure that quizzes are inserted properly before a part begins. -->
-::: { .quiz-end }
-:::
-
-```{=latex}
-\part{key:trustworthy}
-```
diff --git a/book/contents/core/training/training.qmd b/book/contents/core/training/training.qmd
index 3a93ca7b2..50a4b1264 100644
--- a/book/contents/core/training/training.qmd
+++ b/book/contents/core/training/training.qmd
@@ -3196,5 +3196,5 @@ Altogether, the combination of theoretical foundations and practical implementat
 :::
 
 ```{=latex}
-\part{key:optimization}
+\part{key:performance}
 ```
diff --git a/book/part_summaries.yml b/book/part_summaries.yml
index 14ed8ac41..fb111b9a3 100644
--- a/book/part_summaries.yml
+++ b/book/part_summaries.yml
@@ -36,7 +36,7 @@ parts:
     division: "mainmatter"
     type: "part"
     numbered: true
-    title: "Design Principles"
+    title: "Principles"
     description: >
       This part examines the structural composition of machine learning systems. It 
       explores the key components—data pipelines, training processes, and execution 
@@ -45,11 +45,11 @@ parts:
       together, forming the foundation for later discussions on efficiency and 
       deployment.
 
-  - key: "optimization"
+  - key: "performance"
     division: "mainmatter"
     type: "part"
     numbered: true
-    title: "Performance Engineering"
+    title: "Performance"
     description: >
       This part focuses on improving the performance and efficiency of machine learning 
       systems. It explores strategies for accelerating computation, reducing resource 
@@ -61,7 +61,7 @@ parts:
     division: "mainmatter"
     type: "part"
     numbered: true
-    title: "Robust Deployment"
+    title: "Deployment"
     description: >
       This part addresses the transition of machine learning systems from development 
       to real-world operation. It considers the lifecycle of deployed systems, including 
@@ -70,11 +70,11 @@ parts:
       scalable deployment, preparing systems to operate reliably under changing 
       conditions.
 
-  - key: "trustworthy"
+  - key: "responsibility"
     division: "mainmatter"
     type: "part"
     numbered: true
-    title: "Trustworthy Systems"
+    title: "Responsibility"
     description: >
       This part focuses on building machine learning systems that earn and maintain 
       trust through reliable, secure, ethical, and sustainable operation. It explores 
@@ -87,7 +87,7 @@ parts:
     division: "mainmatter"
     type: "part"
     numbered: true
-    title: "ML Systems Frontiers"
+    title: "Frontiers"
     description: >
       This part looks ahead to the emerging frontiers of machine learning systems. 
       It explores new computational paradigms, early-stage innovations, and the