Spring 2027

Data and Algorithms: Ethics and Policy

Introduction to ethical, legal, and policy issues in analytics, big data, and algorithms, focusing on critical analysis of case studies and moral reasoning.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Dane Gogoshin

One student called it the easiest A they had in a long while, while another found the lectures a bit boring but effective. Both rated the course difficulty as low.

What you’ll be able to do

  • Identify, analyze, and evaluate moral problems relevant to data science

  • Distinguish and explain different types of legal and moral concerns including harm, discrimination, procedural unfairness, respect for persons, and democratic legitimacy.

  • Examine and critique the arguments, and to bring original and creative ideas to bear on those arguments in oral and written form.

  • Understand and apply theories and concepts to problems in data and ethics in a variety of domains

  • Demonstrate professional analytic competencies important for management of information organizations

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Sophomore standing. Not open to students with credit forL I S 462.

    • Sophomore standing
    • not eligible with
    take all
LIS 461

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • All of
    • Sophomore standing
    • Not eligible with

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

ALAN RUBEL is recorded teaching in Fall 2020, Fall 2021. Recorded history may be incomplete and does not establish a future schedule.

DANE LEIGH GOGOSHIN is recorded teaching in Fall 2024, Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 1250
LEC 002Online Only0 / 750

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Dane Gogoshin

Students described Dane Gogoshin as knowledgeable and caring, with lectures that explored relevant data privacy topics. Several reviewers found the course manageable and appreciated her flexibility with deadlines.

Recent recorded grades — Spring 2025: 3.90 GPA, 97.2% A/AB (n=422 letter grades); Fall 2025: 3.89 GPA, 96.2% A/AB (n=500 letter grades); Spring 2026: 3.79 GPA, 90.9% A/AB (n=320 letter grades).

difficulty & workload

Past student reviews · Dane Gogoshin

One student called it the easiest A they had in a long while, while another found the lectures a bit boring but effective. Both rated the course difficulty as low.

Past student reviews · Dane Gogoshin

Feedback on class structure was mixed. Some found the large discussion format disorganized and felt time was poorly managed, while others criticized the instructor for dismissing student feedback on layout changes.

Topics

  • Ethical, legal, and policy issues in analytics, big data, and algorithms
  • Major debates and controversies in diverse contexts
  • Application of moral reasoning and legal concepts to case studies

Skills

  • Identify, analyze, and evaluate moral problems relevant to data science
  • Distinguish and explain legal and moral concerns like harm, discrimination, and procedural unfairness
  • Apply ethical theories and concepts to problems in data and ethics across various domains
  • Demonstrate professional analytic competencies for managing information organizations

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

4 earlier forecasts · 0.06 GPA average error.

Grades over time

Through Spring 2027

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

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.

Download part 1
Selected offering source records
LIS 461 · Spring 2027

Data and Algorithms: Ethics and Policy

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:544:025394",
    "course_id": "LIS 461",
    "course_uid": "course_e0809224294be28ec62cda00",
    "term_id": "1274",
    "source_course_id": "025394",
    "source_subject_id": "544",
    "title": "Data and Algorithms: Ethics and Policy",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "9618f1abec4aa1ac49aa43de98a53812060f863a86d1e6ef27f3f0a316c7613e",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}