Case Studies & Template

For more detailed help with two-week case study assignments, review the Two Week Case Analyses Explainer.

Most weeks begin with a particular AI‑in‑society use case presented by the instructor. In class, your group (3–4 students) analyzes the case using the DTPA framework, answering key questions with evidence. Early on, groups focus on one component of DTPA at a time (Data → Tools → Practices); later, groups apply the full framework and are expected to locate additional, related cases (e.g., voting, criminal‑justice, environment) to synthesize across uses.

Alongside the group analysis, each student completes an Individual Effort and Metacognitive Report (~250 words) describing their contribution, learning progress, and any AI use in their work.

Assignment Deadlines & Submission#

You get one (1) drop on the group case analysis; no questions asked. The Individual Effort & Metacognitive Report carries its own separate drop.

If you need an extension, follow the “extenuating circumstances” policy in the syllabus.

How to Use the Template#

Your group should copy the template section (the whole “# 1. Introduction … # 4. Research Resources” block) into a new markdown or Word file for each week’s case analysis. Fill in every heading, cite all sources in APA format, and add a brief group reflection at the end of the “Four Pillars of AI Literacy” section.

Tip: Keep a running bibliography (APA) as you research; you’ll only need to paste the final list into the “Research Resources” section.

Case‑Study Template (to copy for each weekly analysis)#

A version of this template is also available in a Google Doc – copy it here (make a personal copy before editing).

1. Introduction#

• Title: [Insert Case Title]
• AI Use Case: [Brief description of the AI system/application]

Information on the Case#

• Brief Overview: What is the AI system doing? What is its stated purpose?
• Context: Where is it deployed? Who are the primary users? Who are the primary stakeholders affected?

2. DTPA Framework Analysis#

Data#

• Collection: What data is collected and by what means? (e.g., demographics, biometrics, transactions)
• Criteria: Who or what is included/excluded? (e.g., racial, gender, class, age, geographic biases)
• Manipulation/Encoding: How is the data processed? (cleaning, transformation, erasure/addition)
• Bias & Inequity: Does the data reinforce societal biases? Provide concrete examples.

Tools#

• Technical Mechanics: Which algorithms or models are used? (classifiers, LLMs, neural nets, etc.)
• Assumptions: What assumptions are built into the architecture? (data quality, user behavior, etc.)
• Limitations: What are the technical limits? (accuracy, scalability, energy costs, etc.)

Practices#

• Labor: Who created the data and/or the tool? What kind of labor was involved? (manual labeling, outsourcing, etc.)
• Deployment: How is the tool deployed in practice and for what purpose? (who uses it, where, under what conditions)
• Evaluation: How are the practices scrutinized or constrained? (ethical reviews, regulatory oversight)

Actions#

• Outcomes: Did the system achieve its stated goal? If not, why?
• Empowerment/Marginalization: Who benefits or is harmed by the deployment?
• Incentives: What actions does the tool incentivize? (profit, efficiency, compliance, etc.)

3. Four Pillars of AI Literacy#

Reflect on the case through the lens of each pillar. Record any group disagreements and how you resolved them.

Data Literacy#

• Provenance: Where did the data come from? Who contributed?
• Bias & Inequity: How does the data reinforce societal biases?

Quantitative Literacy#

• Algorithmic Reasoning: How can you interpret the technical claims (accuracy, efficiency)?
• Hidden Costs: Energy, labor, or economic costs of training/deployment?

Ethical Literacy#

• Power Dynamics: Who is protected or targeted? Who is forced to be the system’s subject?
• Trade‑offs: What ethical trade‑offs are inherent in the deployment?

Critical Literacy#

• Narratives: How does the system fit into broader AI narratives (“solution” vs. “existential threat”)?
• Pragmatism: How can you balance skepticism with practical judgment to navigate benefits and risks?

4. Research Resources#

• Primary Sources: Reports, datasets, technical papers.
• Secondary Sources: Articles, case studies, reviews.

Similar Cases#

• List other comparable cases you uncovered.
• Briefly compare them to the principal case.

5. Group Reflection (≈ 150 words)#

• Summarize the group’s overall impression of the case, any points of contention, and what you learned about applying the DTPA framework.