Spring 2027

Data and Algorithms: Ethics and Policy (communications Intensive)

An introduction to ethical, legal, and policy issues related to analytics, big data, and algorithms, focusing on critical analysis and communication skills.

offering recorded4 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

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.

  • Communicate ideas orally and in writing.

Source: UW Guide

Grade history

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average GPA
letter grades

No recorded grade history.

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Prerequisites

Course map

Sophomore standing. Not open to students with credit forL I S 461.

    • Sophomore standing
    • not eligible with
    take all
LIS 462

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

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

Calendar & sections

Spring 2027

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SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 1200
DIS 301Classroom Instruction0 / 200
DIS 302Classroom Instruction0 / 200
DIS 303Classroom Instruction0 / 200
DIS 304Classroom Instruction0 / 200
DIS 305Classroom Instruction0 / 200
DIS 306Classroom Instruction0 / 200

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

An introduction to ethical, legal and policy issues related to analytics, "big data" and algorithms to support decision making. Gain familiarity with major debates and controversies in a variety of contexts. Critically analyze course materials and apply moral reasoning and legal concepts to assess case studies and critique arguments made by others. Develop ability to write, present, and communicate complex concepts, evidence, and arguments related to data ethics and policy.

difficulty & workload

No workload feedback recorded.

Topics

  • Ethical, legal, and policy issues in analytics
  • Big data controversies
  • Algorithmic decision-making
  • Legal and moral concerns including harm and discrimination

Skills

  • Moral problem identification and evaluation in data science
  • Differentiating legal and moral concerns such as harm and discrimination
  • Applying ethical theories to diverse data-related problems
  • Oral and written communication of complex concepts

Grades

Instructor

Grade distribution · % of letter grades

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More grade details Grade mix, volume & source data

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

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Selected offering source records
LIS 462 · Spring 2027

Data and Algorithms: Ethics and Policy (Communications Intensive)

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": "1274:544:027319",
    "course_id": "LIS 462",
    "course_uid": "course_7b785ba53c69640f17bea1d6",
    "term_id": "1274",
    "source_course_id": "027319",
    "source_subject_id": "544",
    "title": "Data and Algorithms: Ethics and Policy (Communications Intensive)",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Not Applicable"
  }
]
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Madgrades

Grade history

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Dataset & model

Recorded 2026-10-14

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

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "68e9eccd5b80bcb6b52790566e82a5803d14afcf699f701f16fec5f99424caae",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}