Data Ethics and Society

Instructor Information

• Instructor: Dr. Tomás Rivera (Assistant Professor of Computer Science)
• Email: trivera@university.alkimi.ai
• Office: Ashby Hall 215
• Office Hours:
  • Monday & Wednesday: 3:30–5:00 p.m. (In person, Ashby Hall 215)
  • Friday: 10:00–11:00 a.m. (Virtual via Zoom; link posted in Canvas)
  • Also available by appointment.

Course Description

Ethical, legal, and social dimensions of data collection, algorithmic decision-making, and artificial intelligence, with emphasis on algorithmic bias, data privacy, surveillance capitalism, and governance frameworks. Examination of technical choices, normative values, and societal impacts through philosophical analysis and practical case studies. DS 210 provides an interdisciplinary investigation into the ethical, legal, and social dimensions of data collection, algorithmic decision-making, and artificial intelligence. Expanding upon introductory programming and analytical concepts, students examine how technical choices in data curation, objective formulation, and model deployment embody normative values and generate disparate impacts across marginalized populations. Through rigorous philosophical critique, quantitative fairness auditing, and authentic case studies, students evaluate surveillance capitalism, intellectual property in foundation models, and emerging regulatory governance frameworks.

Prerequisites

CS 150 or sophomore standing. PHIL 305 recommended.

Measurable Student Learning Outcomes

Upon successful completion of DS 210, students will be able to:

1. Analyze Normative Frameworks: Apply major ethical theories (utilitarianism, deontology, virtue ethics, and justice frameworks) to evaluate real-world algorithmic decision systems.
2. Quantify Algorithmic Fairness: Calculate and critique standard statistical fairness metrics (e.g., demographic parity, equalized odds, predictive parity) and articulate their inherent mathematical and philosophical tradeoffs.
3. Evaluate Privacy Architectures: Assess technical and legal data privacy mechanisms, including differential privacy, k-anonymity, GDPR, FERPA, and regional consumer data privacy statutes.
4. Critique Sociotechnical Systems: Conduct comprehensive algorithmic impact assessments on deployed artificial intelligence systems in criminal justice, credit scoring, healthcare diagnostics, and employment screening.
5. Formulate Policy & Governance: Author professional technical policy briefs evaluating generative AI intellectual property rights, data provenance, and responsible disclosure protocols.

Required Course Materials & Approximate Costs

1. Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning: Limitations and Opportunities. MIT Press, 2023. Free digital open access at fairmlbook.org; optional print edition approx. $35.
2. Alkimi Data Ethics Coursepack: Classic Articles, Legal Statutes, and Sociotechnical Case Studies. Free digital access provided via Canvas.
3. Total estimated cost: $0 (open-access digital resources) to $35 (optional print text).

Generative AI Policy (AI Level 2: AI Permitted with Disclosure for Specified Tasks)

In accordance with Alkimi University Academic Policies (Student Handbook §5.8), DS 210 operates under AI Level 2.

• What is permitted: You may use generative AI tools to assist in debugging Python auditing scripts, explore alternative formulations of statistical fairness definitions, and brainstorm potential edge cases for algorithmic impact evaluations.
• What is prohibited: You may not use generative AI to write case study analyses, generate fictional audit data, synthesize policy briefs, or complete midterm examination questions.
• Disclosure: Every submitted case analysis and auditing project must include a dedicated AI usage disclosure describing the specific tools consulted, prompts entered, and how outputs were independently validated.

Grading Components and Weights

• Case Study Analyses (4): 25% (Four analytical case evaluations (1,000–1,200 words each; 6.25% each) analyzing historical instances of algorithmic harm and data misuse)
• Fairness & Bias Auditing Notebooks (3): 20% (Three applied Python notebook exercises auditing datasets for demographic skew and testing algorithmic debiasing interventions)
• Midterm Examination: 20% (In class, Thursday, October 15)
• Seminar Participation & Disclosures: 10% (Active contribution to weekly seminar discussions and reflective evaluations of Generative AI tool disclosures)
• Algorithmic Impact Capstone Project: 25% (Due by the end of the final exam period, Monday, December 14, 2026, 1:00–3:00 p.m.)

Grading Scale (Alkimi University Standard)

• A: 93–100% | A-: 90–92%
• B+: 87–89% | B: 83–86% | B-: 80–82%
• C+: 77–79% | C: 73–76% | C-: 70–72%
• D+: 67–69% | D: 60–66% | F: Below 60%
• Extra credit: No individual extra credit assignments are offered in DS 210. Final grades are calculated strictly from the graded assessment components listed on this syllabus.

Course Schedule (Fall 2026)

Course Policies

Attendance Policy

Because DS 210 functions as an interdisciplinary seminar and hands-on case discussion, regular and punctual attendance is essential. You are permitted two unexcused absences during the semester without academic penalty. Each additional unexcused absence will reduce your final course grade by one third of a letter grade. Missing more than five class sessions will result in an automatic failing grade for the course. Students who miss the first two class meetings without notifying the instructor may be dropped. Students may miss class for religious observance without penalty if they notify the instructor in writing within the first two weeks of the semester (by Wednesday, September 9, 2026). Absences for university-sponsored activities (athletics, performances, conferences) are excused with a letter from the sponsoring office at least one week in advance.

Late Work Policy

Written case analyses and auditing notebook projects lose 10% per calendar day overdue and will not be accepted after three days past the deadline. In-class exercises and examinations cannot be submitted late without advance written approval for an excused absence.

Final Exam Policy

DS 210 has no final examination. The algorithmic impact capstone project takes its place. Nothing is due on Reading Day (Thursday, December 10, 2026).