OCEL.AI Story

The five chapters of our story-driven experiential learning are: 1) life; 2) data; 3) the scientist and AI; 4) users; and 5) the society. Borrowing from data journalism, our overall storyline focuses on “5W + H”: Who is it about? What happened? When did it take place? Where did it take place? Why did it happen? How did it happen. For each chapter, we designed a story framework to guide learners to create their own stories (assignments). 

In addition to the four stories, two calibrations are added to highlight the importance of iterations and experimentations. See Figure 1 for flow of the entire process. 

Chapter 1 Life. The objective of Chapter 1 is to guide students to identify a problem or a challenge facing certain individuals, a group of people, or a community. Students are encouraged to conduct user research, and apply empathetic thinking to create a persona, recreate his or her life settings, and identify unfilled needs. 

Assignment 1 Life” should accurately and passionately describe real-world problems facing the targeted users or audience: 

  • Who are the people or communities in need of help? 
  • What problem happened to them? 
  • When did the problem take place? 
  • Where means two things: 1) The environment and settings that the people or the community is living in, and 2) the place/location where the problem take place. 
  • Why means the possible causes and/or origin of the problem. 
  • How: If you would like, you can add a dimension of how. How did it happen? Sometimes, the answer to how can be covered by what, when and where. 
Calibration 1 takes place between Assignment 1 and Assignment 2. Calibration 1 refers to an iterative process to determine what characteristics or conditions of Story 1 align with Assignment 2. In other words, are the data sets relevant to solving the problem? Or, are we asking the right questions in Assignment 1? 

Chapter 2 Data. The objective of Chapter 2 is to use stories as a hands-on practice to link large-scale data sets with real-world problems identified in Assignment 1. Meanwhile, this is also a process for data cleaning, data preprocessing, and data management. 

Assignment 2 Data” should guide the process of identifying data sets that are most relevant to the real-world problem in Assignment 1 

  • Whois the data set about? Who were sampled in this data set? Who were over sampled or under sampled? Are they representative of the main characters in Assignment 1? Is there any identifiable information or is there any risk of disclose identifiable information? This is fundamentally about the sampling issue, and anonymity. 
  • What events, activities, behaviors, and observations etc. are recorded by the data set? Does the data set record the targeted events, activities, behaviors, etc. in Assignment 1? This is fundamentally about the variables. 
  • Whendid the event, activity, behavior, and observation, etc. take place? When were the data collected? Is it longitudinal or cross-sectional? Are they real time data? How old or fresh are the data? To what extent generalization can be made across time to inform Assignment 1? This is fundamentally about the temporal structure of the data set, and the external validity of the data set across time. 
  • Where did the event, activity, behavior, and observation, etc. take place? Where were the data collected if the information is available? What does the geographical coverage of the data set look like? Does the data set contain geographical information (GIS)? Is this a local, regional, national, or global data set? To what extent generalization can be made across settings to inform Assignment 1? This is fundamentally about geographic variables in the data set, and the external validity of the data set across settings. 
  • Whydid the event, activity, behavior, or observation etc. take place? Why were the data collected?  
  • How: If you would like, you can add a dimension of how. How did it happen? Sometimes, the answer to how can be covered by what, when and where. 

Calibration 2 takes place between Assignment 2 and Assignment 3. This is also an iterative process from data to models. Multiple experimentations are conducted to identify the most accurate and efficient models for given data. 

Chapter 3 The Scientist and AI. The objective of Chapter 3 is to select or create the most relevant ML algorithms given Assignment 1 and Assignment 2. The data sets should be properly aligned so that they can be used in ML. Accordingly, Assignment 3 describes a story about data scientists (the students) and AI together help the people/community find the solution. The main story line is about iterative experimentation that data scientists and AI conduct collaboratively along the journey of “rescuing” people in need. 

Assignment 3 the Scientist and AI” is a story about the data scientist and AI’ selecting the most relevant ML algorithms given Assignment 1 and Assignment 2: 

  • Who: In this story, there are three main characters: 1) the people/the community who needs help, 2) the data scientist (that is you), and 3) AI. 
  • How much does the data scientist understand Assignment 1 (domain) and Assignment 2 (data)? 
  • What models and analysis did the data scientist and AI apply to fulfill the need of the people or the community? 
  • Can the data scientist estimate and select data for their goals from Assignment 1? Can they map data sets from Assignment 2 onto appropriate ML models? 
  • Can the data scientist connect Story 1 with ML models/stories about what a ML model can do? To perform good ML research, what in-depth knowledge and experience with ML algorithms and ML stories does a data scientist need? 
  • When has to do with the iterations (Calibration 2). How much time did it take for experimentation? How efficient is the modeling/algorithm? 
  • Can the data scientist determine the acceptance level of the model (validation with accuracy and runtime performance) considering the targeted users? 
  • Where has to do with the learning environment. Where did this experiential learning process take place? For example, it was part of an online Deep Learning course. 
  • Why explains the modeling. Explainable ML models. 
  • How: If you would like, you can add a dimension of how. How did it happen? Sometimes, the answer to how can be covered by what, when and where. 

Chapter 4 Users. The objective of Chapter 4 is to choose proper ML models for applications considering the targeted users. Accordingly, Assignment 4 uses storytelling to persuade the targeted users or audience that the application can solve their problems, or the visualization discovers knowledge that is important to their decision making. This story will go over through the application/visualization interface design from the user’s perspective, not the developer’s perspective. 

 

Assignment 4 Users” is a story about the user using the application/visualization: 

  • Who: the main character is the targeted user or audience. 
  • What can the application do? What does the visualization show? 
  • When can the user use the application/visualization? 
  • Where will the visualization and applications be deployed, for example, mobile phones, the web, or IoT devices? 
  • Why is the visualization or application useful to the user? 
  • How will the people/the community use this application or visualization to make changes?   

Chapter 5 The Society. This is a story about the impacted population. The impacted population is not always the same as the targeted users. This story encourages the students to examine data science/AI in a broader context of the social and cultural contexts. What are the intended and unintended consequences? In other words, Assignment 5 1) describes ethical issues observed from Chapter 1 to Chapter 4, and 2) discusses and interprets knowledge discovered from the data science/AI process (explainable AI).  

Assignment 5 the Society” is about the impacted population, including ethical, social, and cultural implications: 

  • Who will be impacted? Who were sampled? Who were over sampled or under sampled? Who were the data scientists (yourself and your collaborators)? 
  • What are the social and cultural impacts? What are the concerns about data privacy, security, and fairness? 
  • When will the social and cultural impacts take place? When should people be concerned about data privacy, security, and fairness? 
  • Where will the social and cultural impacts take place? Where will data privacy, security, and fairness issues, like data breach, and evaluative bias, likely to happen? 
  • Why are the social and cultural impacts important or consequential to the people and/or the community? Why should we be concerned about data privacy, security, and fairness issues? 
  • How can we address these societal issues in ML using a community-in-the-loop approach? 

 

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