Computational Ethics for NLP

Summary

As language technologies have become increasingly prevalent, there is a growing awareness that decisions we make about our data, methods, and tools are often tied up with their impact on people and societies. This course introduces students to real-world applications of language technologies and the potential ethical implications associated with them. We discuss philosophical foundations of ethical research along with advanced state-of-the art techniques. Discussion topics include:

• Philosophical foundations: what is ethics, history, medical and psychological experiments, IRB and human subjects, ethical decision making.
• Misrepresentation and bias: algorithms to identify biases in models and data and adversarial approaches to debiasing.
• Privacy: algorithms for demographic inference, personality profiling, and anonymization of demographic and personal traits.
• Civility in communication: techniques to monitor trolling, hate speech, abusive language, cyberbullying, toxic comments.
• Democracy and the language of manipulation: approaches to identify propaganda and manipulation in news, to identify fake news, political framing.
• NLP for Social Good: Low-resource NLP, applications for disaster response and monitoring diseases, medical applications, psychological counseling, interfaces for accessibility.
• Multidisciplinary perspective: invited lectures from experts in behavioral and social sciences, rhetoric, etc.

Announcements

• Please join the class Piazza!
• HW 1 has been released! It is due Feb 11
• Reminder, project proposals are due 2/25
• HW 2 has been released! It is due Mar 3
• HW 3 has been released! It is due April 7
• HW 4 has been released! It is due April 16

Syllabus

The schedule is subject to change. Sign in to Google Drive with your Andrew ID to access the videos.

Additional Resources

• Extended readings list.
• Stanford Ethical and Social Issues in Natural Language Processing course
• University of Washington Ethics in NLP course
• University of Washington Computer Ethics course
• ACL Ethics resources
• Embedded EthiCS at Harvard

Grading

Homework assignments. (4 assignments; 15% each) Each assignment will correspond to a lecture unit: Objectivity and Bias, Civility in Communication, Privacy and Obfuscation, and Computational Social Science.

Each assignment contains a combination of coding, analysis, and discussion. For each assignment, completing the baseline requirements will obtain a passing (B-range) grade. A-range grades can be obtained through completing the open-ended “Advanced Analysis” part of the assignment. Assignments are not necessarily designed to focus on technical solutions, but instead to encourage students to think critically about the course material and understand how to approach ethical problems in NLP, while also allowing for exploration of various methodologies.

Project. (30%) a semester-long (normally) 3-person team project (see below).

Participation in class. (10%) classes will include discussions of reading assignments. Students will be expected to read relevant papers and participate in discussions.

Projects

A major component of this course is a team project. It will be a substantial research effort carried out by each student or groups of students (expected group size = 3; 2-4 is acceptable). See this document for possible project ideas. All assignments are due at 11:59pm on the specified date. The components of the project are:

• Pre-proposal: A 1-2 paragraph document describing the focus area of the project and defining team members (Due 1/23)
• Proposal: (7.5%) A 2-3 page document (ACL format) containing a literature review, concrete problem definition, identification of baseline models, and ideas for final models. Sections should include Introduction, Related Work, Data, Baseline, Proposed Approach. Baselines should be clearly defined but do not need to be implemented yet (Due 2/25)
• Midterm Presentations: (7.5%) An in-class presentation of project and current progress. Presentation should include problem definition, baseline models and results, and description of proposed models (3/19-3/24)
• Final Report and Presentations: (15%) In-class presentations of the project will be held during the final week of class. A final project report will be due the following week. The final report should be formated as a standard research paper with appropriate sections (ACL format, 8 pages) (Presentations 4/28-4/30; Report due 5/8)

Policies

Late policy. Students will have 4 late days that may be used for HW assignments at any point during the semseter. Late days may not be used for project benchmarks. After all late days have been used, no credit will be given for homework submitted late.

Academic honesty. Homework assignments are to be completed individually. Verbal collaboration on homework assignments is acceptable, as well as re-implementation of relevant algorithms from research papers, but everything you turn in must be your own work, and you must note the names of anyone you collaborated with on each problem and cite resources that you used to learn about the problem. The project is to be completed by a team. You are encouraged to use existing NLP components in your project; you must acknowledge these appropriately in the documentation. Suspected violations of academic integrity rules will be handled in accordance with the CMU guidelines on collaboration and cheating.

Note to Students

Take care of yourself! As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of having a healthy life is learning how to ask for help. Asking for support sooner rather than later is almost always helpful. CMU services are available, and treatment does work. You can learn more about confidential mental health services available on campus at: http://www.cmu.edu/counseling/. Support is always available (24/7) from Counseling and Psychological Services: 412-268-2922.

Accommodations for Students with Disabilities:

If you have a disability and have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, I encourage you to contact them at access@andrew.cmu.edu.