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Session 08: Virtue

  • (45 mins) Activity
  • (30 mins) Guest Speaker

(45 mins) Activity

Explain virtue ethics as choosing what you ought to do based on your values and the kind of person you want to be.

Lead students through Dr. Amy Ko’s activity from the University of Washington: Situating Software Organizations. The activity is suitable for data science and machine learning organizations. Visit tables and ask students questions such as:

  • What resources do you think are credible for finding information about this organization?
  • How does this organization get data for data science and machine learning operations?

(30 mins) Guest Speaker

Invite a guest speaker who works in a profession related to machine learning to talk with the students in person or by video call. Prior to the session, ask students to brainstorm questions for the guest speaker.

For a speaker working in industry, some questions that students might ask include:

  • Questions about the speaker’s journey to data science:
    • How did you learn CS?
    • How did you decide to go into Data Science?
    • How does a beginner enter data science/what are some good beginner projects?
    • What are the most valuable skills in your field?
    • What do you like most and least about data science?
    • What about machine learning do you like the most?
  • Questions about the speaker’s projects:
    • I'd like to hear more about the specific research projects within the realm of education that she has worked on, and the outcomes of them.
    • What part of sociology is most interesting to you?
    • What projects has she been recently working on or has in the past?
  • Questions about the speaker’s current work:
    • What do you do on a daily basis?
    • What kinds of jobs do other people do at your company?
    • What are the factors you often consider when creating a machine learning model?
    • Have you ever worked on an ML system for a company? If so, what and how did it perform?
    • Have there been any large mistakes that the AI has made?
    • What are problems that you often encounter after your algorithm has already been implemented?
    • What are some ways that AI programs are improved upon after mistakes are found?
    • How complex is the code for your AI?
    • How exactly is the code for your AI adjusted over time? I assume that the employees are just constantly looking for problems and then they suggest changes to be made, is this how the process of revising the code works?
    • To what extent are ethics an important concept to take in consideration for your company? How is your AI affected by it?
  • Questions about ethics and AI/ML:
    • What kind of biases are inevitable for an "ethical" machine learning program?
    • How do you determine where to draw the line between ethics and productivity
    • What do you think is the most controversial aspect of automating human data? Do you think that it removes the human aspect of it?
    • What is your take on how algorithms can calculate in factors such as diversity and creativity.
    • What do you think is the solution to having an AI that might make mistakes? Do you believe that using AI will always be better than using humans? Is the solution simply to make ""the perfect code"", and if so, is achieving a perfect AI even possible?
  • Questions about other specific interests:
    • Which industry uses machine learning the most?
    • How is machine learning related to the Internet of Things?
    • What education do you recommend if you want to use machine learning for medical purposes?

Next: Deep Learning