AI for the Social Good

Fri, 21 Apr 2023 00:00:00 +0000

Organizers

Dr. Gerrit Großmann, Lisa Dargasz, Kevin Baum, Sarah Sterz, Prof. Verena Wolf.

Please use the seminar assignment system to register. You can also email us to register for the waiting list.

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 the time slot of the seminar during the semester).

Update: The 2023 edition has come to an end. We were privileged to witness the development of three inspiring projects:

Safe Streets: A pedestrian navigation system designed to prioritize safe routes.
Disaster Tweet: A web interface / browser plugin that identifies tweets referencing authentic natural or man-made disasters.
Simple English: A browser plugin that generates summaries of website content in straightforward, easy-to-understand English.

We would like to thank all participating students for their enthusiasm and dedication throughout the seminar.

Topic Overview

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.

Organization

The seminar will take place in person.
The seminar language is English.
The seminar earns you 7 ECTS.
The seminar is eligible for bachelor/master/graduate students (of computer science and related courses).
Registration/unregistration in HISPOS/LSF is due by May 12.
We will meet in room 1.06 (E1.1).

How to Pass

In order to pass the seminar, you need to:

Pass all milestones.
Attend all sessions.
Read papers from the compulsory reading list (resp. watch videos).
Actively contribute to the group work.

Schedule

April 21: Kick-off meeting and group assignment.
May 5: Lecture by Sarah Sterz.
May 9 (Tuesday!): Guest lecture 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).
May 12: In Milestone I, we have a pitch meeting where each group presents three project ideas (we will choose one project to proceed with).
No lecture on May 19 and May 26.
June 2: Informal meeting to discuss Milestone II.
June 9: In Milestone II, each group shows the current status of their projects.
June 16, June 30, July 14: Informal meetings to discuss technical details.
July 21: In Milestone III, each group gives a final presentation on their project.
July 28: In Milestone IV, each group submits a final report.

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.

Grading

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 to answer the questions:

Where does the data come from, and what do you want to do with it?
What is the (degree of) novelty?
Do you contribute to an existing project or do something new?
What similar projects can you learn from, and what is the most relevant scientific literature?
What resources do you need from us? (compute resources, robots, licenses, etc.)

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 and 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 to answer the questions:

What is your concrete plan to finish the project?
What have you identified as 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 do 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

provide a detailed technical description of what you did, and
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 on 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.

Resources

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:

Additionally, 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

Ai Ethics | Neuro-Mechanistic Modeling