Formatting fixes

contents/core/responsible_ai/responsible_ai.qmd

@@ -62,7 +62,7 @@ Putting these principles into practice involves technical techniques, corporate
 
 Machine learning models are often criticized as mysterious "black boxes" - opaque systems where it's unclear how they arrived at particular predictions or decisions. For example, an AI system called [COMPAS](https://doc.wi.gov/Pages/AboutDOC/COMPAS.aspx) used to assess criminal recidivism risk in the US was found to be racially biased against black defendants. Still, the opacity of the algorithm made it difficult to understand and fix the problem. This lack of transparency can obscure biases, errors, and deficiencies.
 
-Explaining model behaviors helps engender trust from the public and domain experts and enables identifying issues to address. Interpretability techniques play a key role in this process. For instance, [LIME](https://homes.cs.washington.edu/~marcotcr/blog/lime/) (Local Interpretable Model-Agnostic Explanations) highlights how individual input features contribute to a specific prediction, while Shapley values quantify each feature’s contribution to a model’s output based on cooperative game theory. Saliency maps, commonly used in image-based models, visually highlight areas of an image that most influenced the model’s decision. These tools empower users to understand model logic.
+Explaining model behaviors helps engender trust from the public and domain experts and enables identifying issues to address. Interpretability techniques play a key role in this process. For instance, [LIME](https://homes.cs.washington.edu/~marcotcr/blog/lime/) (Local Interpretable Model-Agnostic Explanations) highlights how individual input features contribute to a specific prediction, while Shapley values quantify each feature's contribution to a model's output based on cooperative game theory. Saliency maps, commonly used in image-based models, visually highlight areas of an image that most influenced the model's decision. These tools empower users to understand model logic.
 
 Beyond practical benefits, transparency is increasingly required by law. Regulations like the European Union's General Data Protection Regulation ([GDPR](https://gdpr.eu/tag/gdpr/)) mandate that organizations provide explanations for certain automated decisions, especially when they significantly impact individuals. This makes explainability not just a best practice but a legal necessity in some contexts. Together, transparency and explainability form critical pillars of building responsible and trustworthy AI systems.
 
@@ -124,7 +124,6 @@ Edge ML relies on limited on-device data, making analyzing biases across diverse
 
 TinyML poses unique challenges for fairness with highly dispersed specialized hardware and minimal training data. Bias testing is difficult across diverse devices. Collecting representative data from many devices to mitigate bias has scale and privacy hurdles. [DARPA's Assured Neuro Symbolic Learning and Reasoning (ANSR)](https://www.darpa.mil/news-events/2022-06-03) efforts are geared toward developing fairness techniques given extreme hardware constraints.
 
-
 ### Privacy
 
 For cloud ML, vast amounts of user data are concentrated in the cloud, creating risks of exposure through breaches. Differential privacy techniques add noise to cloud data to preserve privacy. Strict access controls and encryption protect cloud data at rest and in transit.
@@ -188,7 +187,6 @@ Extreme decentralization and complexity make external governance infeasible with
 
 : Comparison of key principles in Cloud ML, Edge ML, and TinyML. {#tbl-ml-principles-comparison .striped .hover}
 
-
 ## Technical Aspects
 
 ### Detecting and Mitigating Bias
@@ -197,7 +195,7 @@ Machine learning models, like any complex system, can sometimes exhibit biases i
 
 To evaluate and address these issues, fairness in machine learning is typically assessed by analyzing "subgroup attributes," which are characteristics unrelated to the prediction task, such as geographic location, age group, income level, race, gender, or religion. For example, in a loan default prediction model, subgroups could include race, gender, or religion. When models are trained with the sole objective of maximizing accuracy, they may overlook performance differences across these subgroups, potentially resulting in biased or inconsistent outcomes. 
 
-This concept is illustrated in @fig-fairness-example, which visualizes the performance of a machine learning model predicting loan repayment for two subgroups, Subgroup A (blue) and Subgroup B (red). Each individual in the dataset is represented by a symbol: plusses (+) indicate individuals who will repay their loans (true positives), while circles (O) indicate individuals who will default on their loans (true negatives). The model’s objective is to correctly classify these individuals into repayers and defaulters.
+This concept is illustrated in @fig-fairness-example, which visualizes the performance of a machine learning model predicting loan repayment for two subgroups, Subgroup A (blue) and Subgroup B (red). Each individual in the dataset is represented by a symbol: plusses (+) indicate individuals who will repay their loans (true positives), while circles (O) indicate individuals who will default on their loans (true negatives). The model's objective is to correctly classify these individuals into repayers and defaulters.
 
 To evaluate performance, two dotted lines are shown, representing the thresholds at which the model achieves acceptable accuracy for each subgroup. For Subgroup A, the threshold needs to be set at 81.25% accuracy (the second dotted line) to correctly classify all repayers (plusses). However, using this same threshold for Subgroup B would result in misclassifications, as some repayers in Subgroup B would incorrectly fall below this threshold and be classified as defaulters. For Subgroup B, a lower threshold of 75% accuracy (the first dotted line) is necessary to correctly classify its repayers. However, applying this lower threshold to Subgroup A would result in misclassifications for that group. This illustrates how the model performs unequally across the two subgroups, with each requiring a different threshold to maximize their true positive rates.
 
@@ -265,7 +263,7 @@ Initial unlearning approaches faced limitations in this context. Given the resou
 
 However, newer methods show promise in modifying models to approximately forget data without full retraining. While the accuracy loss from avoiding full rebuilds is modest, guaranteeing data privacy should still be the priority when handling sensitive user information ethically. Even slight exposure to private data can violate user trust. As ML systems become deeply personalized, efficiency and privacy must be enabled from the start---not afterthoughts.
 
-Global privacy regulations, such as the well-established [GDPR](https://gdpr-info.eu) in the European Union, the [CCPA](https://oag.ca.gov/privacy/ccpa) in California, and newer proposals like Canada’s [CPPA](https://blog.didomi.io/en-us/canada-data-privacy-law) and Japan’s [APPI](https://www.dataguidance.com/notes/japan-data-protection-overview), emphasize the right to delete personal data. These policies, alongside high-profile AI incidents such as Stable Diffusion memorizing artist data, have highlighted the ethical imperative for models to allow users to delete their data even after training.
+Global privacy regulations, such as the well-established [GDPR](https://gdpr-info.eu) in the European Union, the [CCPA](https://oag.ca.gov/privacy/ccpa) in California, and newer proposals like Canada's [CPPA](https://blog.didomi.io/en-us/canada-data-privacy-law) and Japan's [APPI](https://www.dataguidance.com/notes/japan-data-protection-overview), emphasize the right to delete personal data. These policies, alongside high-profile AI incidents such as Stable Diffusion memorizing artist data, have highlighted the ethical imperative for models to allow users to delete their data even after training.
 
 The right to remove data arises from privacy concerns around corporations or adversaries misusing sensitive user information. Machine unlearning refers to removing the influence of specific points from an already-trained model. Naively, this involves full retraining without the deleted data. However, connectivity constraints often make retraining infeasible for ML systems personalized and deployed to remote edges. If a smart speaker learns from private home conversations, retaining access to delete that data is important.
 
@@ -358,11 +356,11 @@ Throughout this chapter, we have touched on several key policies aimed at guidin
 * The European Union's [General Data Protection Regulation (GDPR)](https://gdpr-info.eu/) mandates transparency and data protection measures for AI systems handling personal data.  
 * The [AI Bill of Rights](https://www.whitehouse.gov/ostp/ai-bill-of-rights/) outlines principles for ethical AI use in the United States, emphasizing fairness, privacy, and accountability.  
 * The [California Consumer Privacy Act (CCPA)](https://oag.ca.gov/privacy/ccpa) protects consumer data and holds organizations accountable for data misuse.  
-* Canada’s [Responsible Use of Artificial Intelligence](https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai.html) outlines best practices for ethical AI deployment.  
-* Japan’s [Act on the Protection of Personal Information (APPI)](https://www.dataguidance.com/notes/japan-data-protection-overview) establishes guidelines for handling personal data in AI systems.  
-* Canada’s proposed [Consumer Privacy Protection Act (CPPA)](https://blog.didomi.io/en-us/canada-data-privacy-law) aims to strengthen privacy protections in digital ecosystems.  
-* The European Commission’s [White Paper on Artificial Intelligence: A European Approach to Excellence and Trust](https://commission.europa.eu/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en) emphasizes ethical AI development alongside innovation.  
-* The UK’s Information Commissioner’s Office and Alan Turing Institute’s [Guidance on Explaining AI Decisions](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/explaining-decisions-made-with-artificial-intelligence) provides recommendations for increasing AI transparency.  
+* Canada's [Responsible Use of Artificial Intelligence](https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai.html) outlines best practices for ethical AI deployment.  
+* Japan's [Act on the Protection of Personal Information (APPI)](https://www.dataguidance.com/notes/japan-data-protection-overview) establishes guidelines for handling personal data in AI systems.  
+* Canada's proposed [Consumer Privacy Protection Act (CPPA)](https://blog.didomi.io/en-us/canada-data-privacy-law) aims to strengthen privacy protections in digital ecosystems.  
+* The European Commission's [White Paper on Artificial Intelligence: A European Approach to Excellence and Trust](https://commission.europa.eu/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en) emphasizes ethical AI development alongside innovation.  
+* The UK's Information Commissioner's Office and Alan Turing Institute's [Guidance on Explaining AI Decisions](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/explaining-decisions-made-with-artificial-intelligence) provides recommendations for increasing AI transparency.  
 
 These policies highlight an ongoing global effort to balance innovation with accountability and ensure that AI systems are developed and deployed responsibly.