**Assignment 02: Ethics and AI** *Due date found on gradescope* [Back to Neural Networks](http://cs.pomona.edu/classes/cs152/) # Learning Goals - Practice applying ethical frameworks - Practice discussing ethical issues with partners # Grading Walk-Throughs This assignment will be graded as pass/needs-revisions by a TA. To pass the assignment, you must 1. Complete the assignment and submit your work to gradescope. - You should start this assignment in class on the day shown on the calendar. - **Complete the assignment as early as possible**. 2. Schedule a time to meet with a TA prior to the deadline. - You must book a time to meet with a TA - Sign-up on the Google Sheet **with at least 36 hours of notice**. - Contact your TA on Slack after signing-up. - All partners must meet with the TA. If you can't all make it at the same time, then each of you needs to schedule a time to meet with the TAs. 3. Walk the TA through your solutions prior to the deadline. - Walk-throughs should take no more than 20 minutes. - You should be well prepared to walk a TA through your answers. - You may not make any significant corrections during the walk-through. You should plan on making corrections afterward and scheduling a new walk-through time. Mistakes are expected--nobody is perfect. - You must be prepared to explain your answers and justify your assumptions. TAs do not need to lead you to the correct answer during a walk-through--this is best left to a mentor session. 4. The TA will then either - mark your assignment as "pass" on gradescope, or - mark your assignment as "needs-revisions" and inform you that you have some corrections to make. 5. If corrections are needed, then you will need to complete them and then schedule a new time to meet with the TA. - You will ideally complete any needed revisions by the end of the day the following Monday If you have concerns about the grading walk-through, you can meet with me after you've first met with a TA. # Overview For this assignment you will: 1. Form groups of **three** 2. Choose a fictional case study 3. Answer a few questions **prior to reading the study** 4. Read the study 5. Answer the prompts found in the study # Tasks I'd recommend having one partner start by opening up gradescope now and acting as the scribe (the group's not taker). ## Case Studies Here is a quick summary of the six available case studies provided by the Princeton Dialogues on AI and Ethics. You should choose one prior to reading the PDF; your group is welcome to start over and choose a new one if you decide you don't like the one you've chosen after you've started reading it. ### Case Study 1: Automated Healthcare App A team of medical researchers and computer scientists develop an app that utilizes artificial intelligence technologies to make diabetic care easier, more holistic and more accessible. ### Case Study 2: Dynamic Sound Identification An R&D company develop an app that can identify--among other things--personal information about those speaking, links to websites selling a product being advertised on television, encyclopedic entries about bird calls in the wild and other relevant resources. ### Case Study 3: Optimizing Schools A public high school contracts a data science company and gives them access to student behavioral data (attendance, purchases, library usage, movement on campus, etc.) so that they can predict students at-risk of dropping out. ### Case Study 4: Law Enforcement Chatbots A country's federal law enforcement agency teams up with University researchers to develop a chatbot that could be used to identify cybercriminals. ### Case Study 5: Hiring By Machine A group of military veterans create a non-profit company that creates products for veterans transitioning back to civilian life. The company strongly believes in open-source and hiring veterans. ### Case Study 6: Public Sector Data Analytics A once-prosperous city contracts with a consulting group to use an algorithmic, data-driven approach to reduce crime. ## Initial Prompt Prior to reading the case study in full, your group should choose at least three questions to discuss. You do not need to have a complete understanding of the case study or the questions prior to thinking these through. Try to put yourselves in the developers' shoes and guess at any issues that might arise or any background learning that your team might need to conduct. - Questions on oversight and accountability 01. Which laws and regulations might be applicable to this project? 02. How is ethical accountability being achieved? - Questions on data privacy and anonymity 03. How might the legal rights of organizations and individuals be impinged by our use of the data? 04. How might individuals’ privacy and anonymity be impinged via aggregation and linking of the data? - Questions on data availability and validity 05. How do you know that the data is ethically available for its intended use? 06. How do you know that the data is valid for its intended use? - Questions on model bias 07. How have we identified and minimized any bias in the data or in the model? 08. How was any potential modeler bias identified and then, if appropriate, mitigated? - Questions on model transparency and interpretation 09. How transparent does the model need to be and how is that transparency achieved? 10. What are likely misinterpretations of the results and what can be done to prevent those misinterpretations? These questions are from [Integrating Ethics within Machine Learning Courses | ACM Transactions on Computing Education](https://dl.acm.org/doi/10.1145/3341164 "Integrating Ethics within Machine Learning Courses | ACM Transactions on Computing Education") by J. Saltz et al. ## Discussion Questions Case studies include anywhere from three to seven discussion questions. Please read the case study individually, but pause at each discussion question and discuss them as a group. You'll find a place on gradescope to jot down some notes about your discussion, which means you'll want to assign one person to have it open and act as a scribe. Now read through your chosen [case study PDF](https://aiethics.princeton.edu/case-studies/case-study-pdfs/ "Case Study PDFs – Princeton Dialogues on AI and Ethics"). Each case study PDF includes a "Reflections & Discussion Questions" section near the end. You should read through these individually, and you can optionally choose to discuss them as a group. However, you did not need to report on your discussions for this part of the case study PDFs. # Submitting Your Assignment You will submit your code and/or responses on gradescope. **Only one partner should submit.** The submitter will add the other partner through the gradescope interface. To pass the autograder (if one exists for this assignment), your output must exactly match the expected output. Your program output should be similar to the example execution above, and the autograder on gradescope will show you the correct output if yours is not formatted properly. You can use [text-compare](https://text-compare.com/) to compare your output to the expected output and that should give you an idea if you have a misspelled word or extra space (or if I do). Additional details for using gradescope can be found here: - [Submitting an Assignment](https://help.gradescope.com/article/ccbpppziu9-student-submit-work) - [Adding Group Members](https://help.gradescope.com/article/m5qz2xsnjy-student-add-group-members) - [gradescope Student Help Center](https://help.gradescope.com/category/cyk4ij2dwi-student-workflow)