Update

modules/all-about-transparency/_posts/2000-01-02-what-is.md

@@ -2,12 +2,12 @@
 title: What is Algorithmic Transparency
 ---
 
-## Defining algorithmic transparency
+## What is algorithmic transparency
 _Suggested time: 5 minutes_
 
 **Algorithmic transparency** is the principle that information about decisions made by algorithms should be visible to those who use, regulate, and are affected by the systems that employ those algorithms. Algorithmic transparency should enable the monitoring, checking, criticism, or intervention by interested parties. In this course, we use the following broad definition:
 
-> *Algorithmic transparency* refers to the extent to which a human can understand an algorithms<a href="https://www.cambridge.org/core/journals/data-and-policy/article/think-about-the-stakeholders-first-toward-an-algorithmic-transparency-playbook-for-regulatory-compliance/10D7F194DB250DDF3A30471B5CEB9326" target="_blank">(Bell, Nov, Stoyanovich 2023)</a>.
+> *Algorithmic transparency* refers to the extent to which a human can understand an algorithms <a href="https://www.cambridge.org/core/journals/data-and-policy/article/think-about-the-stakeholders-first-toward-an-algorithmic-transparency-playbook-for-regulatory-compliance/10D7F194DB250DDF3A30471B5CEB9326" target="_blank">(Bell, Nov, Stoyanovich 2023)</a>.
 
 <br>
 

modules/all-about-transparency/_posts/2000-01-04-barriers.md

@@ -11,33 +11,39 @@ Researchers and practitioners have made significant advancements in developing t
 
 <!---Importantly, there are two levels of transparency you need to consider, called the **scope of transparency.** The first is **local** transparency, which provides understanding about a single decision made by an algorithm (ex. a single loan applicant), and second is **global** transparency, which explains how an algorithm works overall. Global transparency can give a "bird’s-eye view" of an algorithmic decision-making system, whereas local transparency focuses in on a particular bird or set of birds.-->
 
-# Types of transparency tools
+### Transparency labels and model cards 
 
-- **Transparency labels and model cards** for algorithmic tools are similiar to nutritional labels found in the food industry.  Nutritional labels are designed to be transparent and understandable, and their contents are perceived as a highly credible source of information by consumers, who use them to guide decision making.
+**Transparency labels and model cards** for algorithmic tools are similiar to nutritional labels found in the food industry.  Nutritional labels are designed to be transparent and understandable, and their contents are perceived as a highly credible source of information by consumers, who use them to guide decision making.
 
 There are several examples of transparency labels for algorithms that have been designed by researchers, and like nutritional labels, they often contain the "ingredients" that make up an algorithm. For example, the labels may include descriptive information about the factors considered by the algorithm (the ingredients), how they are ranked in terms of their importance in the decision-making (ingredient ranking), and attributes related to fairness, which could be useful for meeting stakeholder goals related to validity, trust, privacy, and fairness.
 
 > **When is this useful?** To provide a global, high-altitude view of the algorithmic system for aspects like (1) what data is being used by the system, (2) how the system weighs that data, (3) metrics on the performance or fairness of the system overall.
 
-- **Data visualizations** can be used to show information about the data used in to create an algorithmic decision system, or facts about the system itself, like how many decisions an algorithm makes per day, and how many people are affected by those decisions.
+### Data visualizations
+
+**Data visualizations** can be used to show information about the data used in to create an algorithmic decision system, or facts about the system itself, like how many decisions an algorithm makes per day, and how many people are affected by those decisions.
 
 Visualizations have proved useful for informing users and making complex information more accessible and digestible, and have even been found to have a powerful persuasive effect. Visualizations are often an advisable tool for transparency as they can easily convey lots of information in a simple manner, and organizations commonly staff analysts who specialize in visualizations.
 
 It's also important that visualizations are designed thoughtfully, as they have the ability to be abused and can successfully misrepresent a message through techniques like exaggeration or understatement.
 
 > **When is this useful?** Useful for presenting complex information in a digestable way, particularly for non-technical users. This could include both internal and external stakeholders, like humans-in-the-loop for the former and affected individauls for the latter.
 
-- Some (but not all) algorithms have built in **intrinsic transparency mechanisms** _(also called intrinsic explainability mechanisms)_ that simply need to be surfaced to offer transparency into how they work.
+### Intrinsic transparency mechanisms
+
+Some (but not all) algorithms have built in **intrinsic transparency mechanisms** _(also called intrinsic explainability mechanisms)_ that simply need to be surfaced to offer transparency into how they work.
 
 For example, two common algorithm types are **decision trees** and **rules-lists.** For the former it is possible to print out and display the tree diagram for the user. For the latter, one can list out all the rules used to make a decision for. Another type of commonly used algorithm are **linear models**, which can produce formulas that explain their decision-making. These formulas are sometimes very easy to understand.
 
 Unfortunately, many highly sophisticated algorithms like **random forests** and **neural networks** do ** have intrinsic transparency mechanisms. Importantly, the practitioners who designed the algorithm will be aware of whether or ** intrinsic transparency mechanisms are available.
 
 > **When is this useful?**  To answer the question ``how does the system work, to the extent that given a new input to the algorithm, I could anticipate the output with a high degree of accuracy?'' Generally for providing a deeper understanding of how the underlying algorithm in the system functions.
 
-> _Decision trees^, rules-lists^, linear models^, random forests, and nueral networks_ are all examples of types of AI algorithms. If you are non-technical, it is *not* important that you understand what they do or how they work -- your technical team will! For now, just know that the algorithms marked with the carrot _^_ are _more transparent_ than others.
+<!-- >> _Decision trees^, rules-lists^, linear models^, random forests, and nueral networks_ are all examples of types of AI algorithms. If you are non-technical, it is *not* important that you understand what they do or how they work -- your technical team will! For now, just know that the algorithms marked with the carrot _^_ are _more transparent_ than others. -->
 
-- The **attribute importance** _(also called feature importance or factor importance)_ of an algorithm is a list that shows all the different attributes (sometimes called features or factors) that are considered by an algorithm, and their relative weights. It offers **global transparency** for an algorithm.
+### Attribute importance
+
+The **attribute importance** _(also called feature importance or factor importance)_ of an algorithm is a list that shows all the different attributes (sometimes called features or factors) that are considered by an algorithm, and their relative weights. It offers **global transparency** for an algorithm.
 
 For example, consider an algorithm that makes predictions on whether or ** an individual should receive a loan. The attribute importance could be made up of three attributes: an individual's income, their credit history, and their education level. The weights for these attributes in the algorithm’s decision-making may be 40% income, 40% credit history, and 20% education level.
 
@@ -48,7 +54,9 @@ There are three benefits of attribute importance:
 
 > **When is this useful?** Provides a global understanding of how an algorithmic system is processing data at a slightly deeper level than what is often found in transparency labels. Useful for __learning and support__ and to some extent __recourse__. Useful to practitioners for checking the __validity__ of an algorithmic system.
 
-- The **attribute influence** _(also called feature influence or SHAP factors))_ of an algorithm is similar to the attribute importance, except that it shows how the attributes of a single instance or individual impacted the algorithm's output. In contrast to attribute importance, the influence shows the **local transparency** for a particular case. Like with attribute importance, the attribute influence can be created for _any_ algorithm. 
+### Attribute influence
+
+The **attribute influence** _(also called feature influence or SHAP factors))_ of an algorithm is similar to the attribute importance, except that it shows how the attributes of a single instance or individual impacted the algorithm's output. In contrast to attribute importance, the influence shows the **local transparency** for a particular case. Like with attribute importance, the attribute influence can be created for _any_ algorithm. 
 
 For example, consider again an algorithm that makes predictions on whether or not an individual should receive a loan. If an individual is rejected for a loan, the attribute influence could tell them _your high income and education level were influencing the loan decision from the algorithm positively, but ultimately your low credit score caused the algorithm to reject your loan application._