Spring 2027

Introductory Artificial Intelligence (ai) and Data Ethics

Introductory course on AI and Data Ethics covering moral/political issues, technical concepts like bias/variance tradeoff, and applications in medicine and law.

offering recorded3–4 credits
Recorded instructors · Spring 2027 Dylan Beschoner

Summary

1 / 6

Past student reviews · Burgandy Basulto

Past students found the workload easy and the grading lenient, with generous extensions. The final assessment was an essay, allowing students to choose between an argumentative piece or a media connection essay.

What you’ll be able to do

  • Critically evaluate ethical arguments in relation to data and artificial intelligence (AI).

  • Interpret complex texts accurately and analyze them logically by identifying explicit and implicit background assumptions, and by understanding which conclusions necessarily follow from them.

  • Discuss controversial ethical and social questions respectfully and with the aim of uncovering the truth.

  • Identify and evaluate the ethical, legal, and political implications of artificial intelligence (AI), as well as their historical and societal context.

Source: UW Guide

Grade history

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average GPA
letter grades
A
AB
B
BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

Sophomore standing

  • Sophomore standing
PHILOS 244

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

Prerequisite text tree
  • Sophomore standing

Professors

Spring 2027
Historical instructors & teaching patterns

BURGANDY BASULTO is recorded teaching in Fall 2025. 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 / 2070
DIS 301Classroom Instruction0 / 230
DIS 302Classroom Instruction0 / 230
DIS 303Classroom Instruction0 / 230
DIS 304Classroom Instruction0 / 230
DIS 305Classroom Instruction0 / 230
DIS 306Classroom Instruction0 / 230
LEC 002Classroom Instruction0 / 690
DIS 321Classroom Instruction0 / 230
DIS 322Classroom Instruction0 / 230
DIS 323Classroom Instruction0 / 230
DIS 307Classroom Instruction0 / 230
DIS 308Classroom Instruction0 / 230
DIS 309Classroom Instruction0 / 230
DIS 324Classroom Instruction0 / 00
DIS 325Classroom Instruction0 / 00
DIS 326Classroom Instruction0 / 00

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 · Burgandy Basulto

Historical feedback described the course as basic and manageable, with topics that felt common-sense rather than groundbreaking. Students noted the material was self-explanatory and the class structure was straightforward.

Recent recorded grades — Fall 2025: 3.96 GPA, 98.3% A/AB (n=121 letter grades); Spring 2026: 3.94 GPA, 97.8% A/AB (n=138 letter grades).

difficulty & workload

Past student reviews · Burgandy Basulto

Past students found the workload easy and the grading lenient, with generous extensions. The final assessment was an essay, allowing students to choose between an argumentative piece or a media connection essay.

Past student reviews · Burgandy Basulto

Experiences varied: one student found the class unengaging with random participation calls, while another described the instructor as extremely knowledgeable and helpful. Readings were summarized in class, making them feel less essential.

Topics

  • Basic technical concepts including bias/variance tradeoff, reference class problem, and inductive risk.
  • Data privacy, automation's societal impacts, and algorithmic use in medicine and criminal law.
  • Artificial Intelligence and Data Ethics.

Skills

  • Critically evaluating ethical arguments related to AI and data.
  • Logical analysis of complex texts and identification of assumptions.
  • Evaluating ethical, legal, and political implications of AI.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

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.

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Selected offering source records
PHILOS 244 · Spring 2027

Introductory Artificial Intelligence (AI) and Data Ethics

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": "1274:736:027102",
    "course_id": "PHILOS 244",
    "course_uid": "course_d271e7d703bce2000629b9ba",
    "term_id": "1274",
    "source_course_id": "027102",
    "source_subject_id": "736",
    "title": "Introductory Artificial Intelligence (AI) and Data Ethics",
    "credits_min": 3,
    "credits_max": 4,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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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": "2be8a3d293544909a92cdeeb0e102a51e7bf1854e3f7c6012cbf56e12b27bcb1",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}