ESE 3590-Signals, Data, and Equity

Description

This course introduces the design of classification and estimation systems for equity, that is, with the goal of reducing the inequities of racism, sexism, xenophobia, ableism, and other systems of oppression. Systems which change the allocation of resources among people can increase inequity due to their inputs, the systems themselves, or how the systems interact in the context in which they are deployed. This course presents background in power and oppression, to help predict how new technological and societal systems might interact, and when they might confront or reinforce existing power systems. Measurement theory, the study of the mismatch between a system's intended measure and the data it actually uses, is covered. Multiple example sensing and classification systems which operate on people are covered by implementing algorithms and quantifying inequitable outputs.

The course has (loosely) three sections:

I. Space-shaping & context
II. Medical systems
III. Locality & indigeneity.

Timings

• Class: MW 10-11:20am in TBD.

• Office hours: Tu 4-5pm, W 11:30-12:30pm in Green 2155.

Resources

The course will draw from many resources listed on Canvas and (to a more limited extent) in the course schedule below.

Safe + Brave

This is a discussion-based course where we will all commit to cultivating a safe and brave environment for all students to participate in. We use the five pillars of Arao and Clemens to frame what such a space consists of:

• “Controversy with civility” where varying opinions are accepted,

• “Owning intentions and impacts” in which participants acknowledge and discuss instances where a dialogue has affected the emotional well-being of another person,

• “Challenge by choice” where participants have an option to step in and out of challenging conversations,

• “Respect” where students show respect for one another’s basic personhood,

• “No attacks” where students agree not to intentionally inflict harm on one another.

You can also feel very free to call me out on words or actions that hinder these aims. Here is an anonymous google form where you can let me know about any concerns.

Assessment

• Projects (60%)

  • • two "what could go wrong?" reports - due 9/22 and 10/13 (20%)

    • partly in-class lab - due 10/20 (20%)

    • website project - due 12/7, late due date 12/10 (20%)

• Participation (30%)

  • • preparing for and engaging in discussion (10%)

    • self-reflections (10%)

    • attendance (10%)

• Conversation / oral exam - during the week of 10/28 (10%)

Completion:

• Missing one reflection won't impact your grade. After this point, each missed reflection counts as -1% up to -10%.

• Five unexplained class absences won't impact your grade. After this point, each unexplained absence counts as -1% up to -10%. 

Virtual testing: I will offer a virtual option for attending class, assessed conversations, and other sharing moments in circumstances such as quarantine due to sickness or potential exposure to covid-19.

Late submission: Any deadlines are soft in the sense that work can still be submitted up to one hour after the deadline has passed but may receive a 10% scalar penalty (i.e. if you score 90% it may count as 81%).

Makeups: Because of the features above makeups are not be permitted under any circumstances.

Integrity

• Attempting to cheat in this course is unacceptable and will be strongly penalised. A first offense will be penalised with a zero grade on the relevant piece of assessment. A second offense will be penalised with an immediate fail grade.

• Collaboration is permitted (actually encouraged!) on homework assignments, however each student must write up solutions in their own words.  Please write the names of any other students you have collaborated with at the top of each assignment. Significant similarities between submissions from different students that fail to mention any collaboration counts as an act of cheating and will be penalised as such.

Other Information

• This is a 3 credit class.

• There is no online option for the class except as described above for assessment.

• Prerequisites: CSE 2107 - Introduction to Data Science OR CSE 4107 - Introduction to Machine Learning OR ESE 1050 - Introduction to Electrical and Systems Engineering OR ENGR 1201 Introduction to Scientific Computing OR Permission of Instructor.

• Final letter grades will be distributed according to the scale A- 90%, B- 80%, C-70%, D-50%. There are no minuses in the course and pluses will be distributed at discretion of the instructor to recognize outstanding academic engagement and/or contributions to class community.

• Attendance to class or recitation is not required but, as with any advanced STEM class, will usually impact your learning. Notes from class will be posted each week.

• I encourage you to discuss with me, my department chair, or your academic advisor(s) about any concerns you have around classroom dynamics.

• This class will involve a meaningful amount of coding in either Matlab or Python (your choice).

Relevant Policies

• In all academic work, the ideas and contributions of others (including generative artificial intelligence) must be appropriately acknowledged and work that is presented as original must be, in fact, original. You should familiarize yourself with the appropriate academic integrity policies of your academic program(s).

• Except as otherwise expressly authorized by the instructor or the university, students may not record, stream, reproduce, display, publish or further distribute any classroom activities or course materials. This includes lectures, class discussions, advising meetings, office hours, assessments, problems, answers, presentations, slides, screenshots or other materials presented as part of the course.  If a student with a disability wishes to request the use of assistive technology as a reasonable accommodation, the student must first contact the Office of Disability Resources to seek approval. If recording is permitted, unauthorized use or distribution of recordings is also prohibited.  

• WashU supports the right of all enrolled students to an equitable educational opportunity and strives to create an inclusive learning environment. In the event the physical or online environment results in barriers to your inclusion due to a disability, please contact WashU’s Disability Resources (DR) as soon as possible and engage in a process for determining and communicating reasonable accommodations. As soon as possible after receiving an accommodation from DR, send me your WashU Accommodation Letter. Remember that accommodations cannot be applied retroactively.

• If you are a victim of sexual discrimination, harassment or violence, we encourage you to speak with someone as soon as possible. Understand that if you choose to speak to your instructor, they must report your disclosure to their department chair, dean, or the Gender Equity and Title IX Compliance Officer, which may trigger an investigation into the incident. You may also reach out to the Relationship & Sexual Violence Prevention (RSVP) Center to discuss your rights and your options with individuals who are not mandatory reporters -- further resources are available here.

• To ensure that accommodations may be made for students who miss class, assignments, or exams to observe a religious holiday, you must me over email before the end of the third week of class, or as soon as possible if the holiday occurs during the first three weeks of the semester. For more information, please see the university's Religious Holiday Class Absence Policy.

• Before an emergency affects our class, students can take steps to be prepared by downloading the WashU SAFE App.  In addition, each classroom contains a “Quick Guide for Emergencies” near the door.

• WashU provides many resources to support services that address academic, personal, and professional needs.  You can explore resources that might help you here.