Term: Aug - Dec’ 26

Time: MW: 2 – 3:30 PM

Venue: CDS 419

Credits: 3:0

Course Details

Outline: We interact with AI technology on a daily basis—such systems answer the questions we ask (using Google, or other search engines), curate the content we read, unlock our phones, allow entry to airports, etc. Further, with the recent advances in large language and vision models, the impact of such technology on our lives is only expected to grow. This course introduces students to ethical implications associated with design, development and deployment of AI technology spanning NLP, Vision and Speech applications.

In this course, most classes would be a discussion based on the readings assigned for that particular day. These classes would begin with a short quiz, which would be straightforward if you have read through the required section of the reading material. The in-class discussion among students would be facilitated by the instructor and teaching assistants who would bring in discussion points based on the reading material. In addition, starting from this semester, we will hold a few regular instructor-led classes.

Prerequisites: The class is intended for graduate students and senior undergraduates. Students would most benefit if they have finished at least a basic machine learning course from IISc (or similar-quality course elsewhere) and any one courses related to the discussed applications (computer vision, speech or NLP). There are no hard pre-requisites, we trust students to self assess whether this course is for them.

Content: Specifically, this seminar course would facilitate discussions among students structured around pre-selected readings on topics related to ethics in AI. Specifically, we plan to read about and discuss following modules:

• M1. Overview of ethical theories
• M2. Data collection and curation
• M3. Biases and algorithmic fairness; debiasing and mitigating harms
• M4. AI for Global South
• M5. Content Moderation: Misinformation, disinformation and hate-speech
• M6. Understanding AI systems: transparency, accountability, (lack of) interpretability
• M7. Alignment and Safety of AI systems
• M8. Implications of AI-generated Content
• M9. Environmental impact of model training and inference
• M10. Future of work; economic impact of AI

Intentionally, many of the modules are connected to each other. We hope to spend at least 2-4 classes discussing each module.

Schedule:

This is tentative schedule and subject to change.

Course Evaluation

The evaluation comprises 3 components:

• Class projects [50%]: we will shortly share more details about the course projects
• Readings & discussion [20%]: In-class quizzes, we would drop the lowest two scores.
• Final exam [30%]. There would be an exam towards the end of the course.

Class Projects

The course project constitutes 50% of the overall score, where students (with team sizes of no more than two) get a chance to apply the acquired knowledge. The teaching staff would be keenly, and closely, involved in these projects, and students would have a choice to pick projects from a list of planned and scoped ideas. Additionally, students can also work on a project of their choice so long as the project is related to (a) AI, and (b) there are ethical aspects associated with it that merit serious discussion. These projects could take diverse forms, including case studies, qualitative research projects, model and/or data audits, algorithmic inquiries and solutions, etc. The project includes three milestones: (1) proposal along with a literature survey, which clearly states the problem, discusses relevant literature and outlines a rough action plan; a (2) mid-term report and a (3) final report. Towards the end of the course, students would get a chance to showcase their research through presentations or posters.

Late days: Each team would get three late days for projects, no extensions will be offered (please don’t even ask). After your late days expire, you can still submit your project but your obtained score would be divided by 2 if submitting after 1 day, and will be divided by 4 if submitting after 2 days. No submissions would be entertained after that.

Important Dates:

• Sept 11, 2026 Project proposals due 16:59 PM (IST). Please see instructions here.
• Oct 16, 2026 Mid reports due 16:59 PM (IST)
• Nov 20, 2026 Final reports due 16:59 PM (IST)

Discussions & (Anonymous) Feedback

We will use email for all discussions on course-related matters. If you have any feedback, you can share it (anonymously or otherwise) through this link: http://tinyurl.com/feedback-for-danish

Teaching Staff

1. Arati Mohapatra (Teaching Assistant) (OH: TBD; Venue: TBD; notify them)
2. Danish Pruthi (Instructor) (OH: TBD; Venue: CDS 401; notify them)

If you are planning to visit office hours, please notify us a day prior.

Acknowledgements

We are grateful to Yulia Tsvetkov for readily sharing the content for her ethics classes at CMU and UW, and to Kinshuk Vasisht and Navreet Kaur for helping with the initial outline of this course.