11-830: Computational Ethics for NLP

Spring 2022

• Time: 10:10-11:30 Tuesdays & Thursdays
• Place: Singleton Room, Roberts Engineering Hall [PDF map]
• Zoom: See Canvas or Slack for Zoom link.

This page is for the Spring 2022 version of the class. You can find the website for Spring 2023 here!

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 & security : 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.

Syllabus is subject to change, this will always be the latest version.

Week	Date	Theme	Topics	Readings	Assignments
1	1/18	Introduction	Motivation, requirements, and overview	Barocas & Selbst (2016) Hovy & Spruit (2016) Jin et al. (2021)
	1/20	Project Introductions	Project expectations and ideas
2	1/25	Foundations	Philosophical foundations & history
	1/27	Foundations	Philosophical foundations & history		Project pre-proposal due.
3	2/1	NLP for Social Good	Invited talk – Alex Hauptmann
	2/3	Objectivity and Bias	Stereotypes, prejudice, and discrimination: background	Buolamwini & Gebru (2018) Stanovsky et al. (2019) Birhane et al. (2021)	HW 1 released.
4	2/8	Objectivity and Bias	Invited talk – Anjalie Field	Field et al. (2021) Field et al. (2022)
	2/10	Objectivity and Bias	Bias detection and debiasing of data, algorithms, and models	Zhao et al. (2017) Sun et al. (2019) Ahia et al. (2021)
5	2/15	Objectivity and Bias	Invited talk – Sara Hooker	Hooker (2021)	HW 1 due.
	2/17	Civility in communication	Bias detection and debiasing of data, algorithms, and models	Chouldechova (2017) Jurgens et al. (2017)
6	2/22	Civility in communication	Invited talk – Maarten Sap	Sap et al. (2019) Sap et al. (2020) Ma et al. (2020)
	2/24	Civility in communication	Safe dialogue systems – Prakhar Gupta		HW 2 released. Project proposal due Fri, 2/25.
7	3/1	Civility in communication	Identification of trolling, hate speech, abusive language, toxic comments	Warner & Hirschberg (2012) Schmidt et al. (2017) Jurgens et al. (2019)
	3/3	Language of Manipulation	Propaganda
8	3/8	Spring break
	3/10	Spring break
9	3/15	Language of Manipulation	Invited talk – Rayid Ghani
	3/17	Language of Manipulation	Fake news and influencing elections		HW 2 due, HW 3 released.
10	3/22	Midterm project presentations
	3/24	Midterm project presentations
11	3/29	Privacy, Profiling, Security	Privacy and anonymity, Cambridge Analytica discussion	Guardian article
	3/31	NLP for Social Good	Endangered languages		HW 3 due.
12	4/5	NLP for Social Good	Invited talk - David Widder	AI For Good Is Often Bad The Invention of “Ethical AI” Predictive policing in Pittsburgh	HW 4 released.
	4/7	No class
13	4/12	NLP for Social Good	NLP for child welfare – Nupoor Gandhi
	4/14	AI and Climate Change	AI and the climate, the hardware lottery, efficient NLP
14	4/19	Intellectual Property	Plagiarism and plagiarism detection. Patents.		HW 4 due.
	4/21	Discussion	Code of Ethics
15	4/26	Final project presentations
	4/28	Final project presentations
	5/6				Final project report due

Grades are based on a combination of homeworks and participation (individual) and a semester-long class project (group). All assignments are due at 11:59pm on the specified date.

• Homework assignments. (4 assignments; 15% each) 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 3-person team project (more details below and in class).

• Participation. (10%) classes will include discussions of reading assignments. Students will be expected to read relevant papers and participate in class discussions. Participation points can also be earned by posting interesting questions and useful answers on Slack.

A major component of this course is a team project. It will be a substantial research effort carried out by each group of students (expected group size = 3; 2-4 is acceptable). You can find some project ideas and resources here. Please use the #find-project-groups channel on Slack to find classmates with complementary interests and form groups. There will be a number of project milestones throughout the semester:

• Pre-proposal: A 1-2 paragraph document describing the focus area of the project and defining team members.
• 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.
• 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.
• 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).

Late policy. Each student will have 4 total 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.

Accommodations for students with disabilities. If you have a disability and have an accommodations letter from the Disability Resources office, we encourage you to discuss your accommodations and needs with the instructors as early in the semester as possible. We 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, we encourage you to contact them at access@andrew.cmu.edu.

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. This is normal, and all of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of 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 free to students, and treatment does work. You can learn more about confidential mental health services available on campus through Counseling and Psychological Services (CaPS). Support is always available (24/7) at: 412-268-2922.

Take care of your classmates and instructors! In this class, every individual will and must be treated with respect. The ways we are diverse are many and are fundamental to building and maintaining an equitable and an inclusive campus community. These include but are not limited to: race, color, national origin, caste, sex, disability (visible or invisible), age, sexual orientation, gender identity, religion, creed, ancestry, belief, veteran status, or genetic information.

Research shows that greater diversity across individuals leads to greater creativity in the group. We at CMU work to promote diversity, equity and inclusion not only because it is necessary for excellence and innovation, but because it is just. Therefore, while we are imperfect, we ask you all to fully commit to work, both inside and outside of our classrooms to increase our commitment to build and sustain a campus community that embraces these core values. It is the responsibility of each of us to create a safer and more inclusive environment. Incidents of bias or discrimination, whether intentional or unintentional in their occurrence, contribute to creating an unwelcoming environment for individuals and groups at the university. If you experience or observe unfair or hostile treatment on the basis of identity, we encourage you to speak out for justice and offer support in the moment and/or share your experience using the following resources:

• Center for Student Diversity and Inclusion: csdi@andrew.cmu.edu, (412) 268-2150
• CMU anonymous reporting hotline, (844) 587-0793