Ethics of Data for Engineers
Introduces ethical issues in data engineering, covering algorithmic fairness, differential privacy, and robustness for engineers.
Summary
Past student reviews · Kangwook Lee
Homework assignments were long but fair, while exams were described as intimidating due to concise problem statements, though grading was lenient.
What you’ll be able to do
Describe the importance of ethical data science/engineering
Identify challenges of trustworthy data use in engineering such as fairness, privacy, and robustness
Apply the definitions of trustworthy data engineering to real-world datasets
Analyze the data analysis pipelines and evaluate the trustworthiness of their outcomes
Create proper data analysis pipelines with ethical considerations
Implement cutting-edge techniques to enhance the fairness, privacy, and robustness of data analysis processes
Conduct independent research on emerging challenges in ethical data engineering
Source: UW Guide 1· UW Guide 2
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course map(I SY E 521, 562,M E/COMP SCI/E C E 532, or 539) and (E C E 331,MATH/STAT 309,STAT 311,MATH 331, or STAT/MATH 431), or graduate/professional standing
- take one
- take one
- graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- All of
- graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Past student reviews · Kangwook Lee
Prof. Kangwook Lee delivered clear, informative lectures on machine learning that were math-heavy but supported by his high accessibility and care for student learning.
Recent recorded grades — Spring 2024: 4.00 GPA, 100.0% A/AB (n=7 letter grades); Spring 2025: 3.85 GPA, 100.0% A/AB (n=10 letter grades).
difficulty & workload
Past student reviews · Kangwook Lee
Homework assignments were long but fair, while exams were described as intimidating due to concise problem statements, though grading was lenient.
Past student reviews · Kangwook Lee
Students found Prof. Lee very accessible outside of class and online for help, noting that he clearly cared about their learning despite the course's mathematical depth.
Topics
- Ethical issues in data engineering
- Algorithmic fairness: individual, group, and counterfactual fairness
- Differential privacy and applications
- Robustness in data engineering
Skills
- Identify and apply algorithmic fairness concepts including individual, group, and counterfactual fairness.
- Apply differential privacy techniques to enhance the privacy of data analysis processes.
- Create data analysis pipelines that incorporate ethical considerations.
- Analyze data analysis pipelines to evaluate the trustworthiness of their outcomes.
Grades
Latest available · Spring 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2025
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "793ac484d11f6f06cd989639f237e3a12abb6537059590df7bef284653600ef9",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}