Dr. Gerrit Großmann
Lisa Dargasz, BA
Kevin Baum, MSc, MA
Sarah Sterz, MA
Prof. Verena Wolf
Please use the seminar assignment system to register. You can also email us to register for the waiting list (only in case you were unlucky and were not selected by the seminar assignment system, but are really eager to attend).
For any issues regarding the seminar, please e-mail Gerrit Großmann and have [SocialGoodSeminar] in the subject line.
The kick-off meeting will be on Friday, April 21, at 2 PM (sharp) (this is also be the time slot of the seminar during the semester).
Update:
The 2023 edition of the AI for Social Good seminar has come to an end. We were privileged to witness the development of three inspiring projects:
We would like to thank all participating students for their enthusiasm and dedication throughout the seminar.
Are you an AI enthusiast eager to make a real difference beyond just theoretical knowledge? Do you aspire to use the latest AI technologies to tackle some of humanity’s most pressing issues and make the world a better place? If so, we invite you to join us for “AI for the Social Good”. Our goal is to equip you with both theoretical and practical knowledge to create AI solutions to real-world problems. You’ll have the opportunity to collaborate with other participants in a hands-on project to make a difference in your community or the world at large.
May 12.room 1.06 (building E1.1).In order to pass the seminar, you need to:
Collected for Profit, Repurposed for Social Good: What Advertising Data Reveals About Society by Ingmar Weber, 14:15 - 16:00, in lecture hall 003 (E1.3).On Fridays without a Milestone, you will either meet with your supervisor, or we will have a guest lecture, or there will be no meeting at all.
Each student will receive an individual grade. Grading is based on the work of the group (e.g., how suitable are the project ideas) and the work of the student within the group (e.g., indicated by group discussions or Gitlab contributions) More specific requirements and evaluation criteria are listed below for each Milestone.
Milestone I - Coming up with project ideas:
Every group is expected to come up with three project ideas. We will pick one of these for the group to proceed. Your task is to pitch these project ideas and convince us they all fulfill the requirements.
For once, each project should be “socially good” in a reasonable sense. That is, it can likely be used to prospectively impact society (or a subset of society) in a positive and meaningful way. Moreover, each project should be related to artificial intelligence, deep learning, or data science. That is, you work with data that you find, create or collect, and you infer knowledge from this data.
Your project idea does not have to be completely novel. You can also extend or improve an existing project. Provide an open-source alternative to a commercial product, or adapt an existing project to specific challenges.
In the pitch meeting, you should be able two answer the questions: -Where does the data come from, and what do you want to do with it?
The presentation should be around 5 minutes per project.
Milestone II - Finalizing your strategy:
Milestone II should fall into the period where you have finalized your ideas start implementing your project. Ideally, you can already present a prototype implementation or solution to a toy problem. The presentation allows you to receive some last-minute feedback and critique before completely committing to your plan. You should be able two answer the questions: What is your concrete plan to finish the project? What have you identified s the main challenges Where do you need further input? What ethical concerns do you already have? How do you plan to finish the project in the given time interval? The presentation should be around 10 minutes.
Milestone III - Present your results:
Milestone III is the final presentation at the end of the semester that rounds up everything: What have you done? What did work, and what did not? Was your project an overall success or failure? What have you learned, and what would you make differently next time? The presentation should be around 20 minutes.
Milestone IV - Reflect on your impact and contextualize your project:
Milestone IV consists of a group submission and an individual submission.
In the 5 - 10 pages group report, you should 1) provide a detailed technical description of what you did, and 2) judge your project from an ethical point of view? Is it actually social good? In which sense of social good? Can it be misused? You can use the compulsory reading list as guidance.
In addition, all participants must turn in an individual report (roughly one page) on their own explaining what work they did in the project and what they learned. You can also provide overall feedback of the seminar.
Workload:
You will receive seven credit points (CP) for the seminar. Each CP corresponds to a workload of 30 hours. To give you a rough idea of our expectations, we suggest the following time management:
Spend 1 - 2 CP (30 - 60 hours) on the project proposals, 3 - 5 CP (90 - 150 hours) on the actual implementation and attendance, and 1 CP (30 hours) on the final report and the presentations.
Compulsory reading/watch list:
Everybody has to read/watch at least three papers/videos. In each group, each paper/video must be read/watched by at least one person. Do this before Milestone II.
Texts:
Videos:
Further optional reading:
Example datasets:
Recall that you can always collect/label your own datasets.
Example projects and ideas:
Aditionally, correlaid, the DSSG repo list, and datakind might provide useful ideas. We also suggest that you draw on your own life experience for inspiration.
Image credit: SpaceX