Syllabus

Welcome to DATASCI 185! This course offers a non-technical introduction to artificial intelligence and its effects on institutions, work and everyday life. The course is practical: students will learn how modern systems are designed and how to ask the right questions about data, reliability and harms. The course combines short demonstrations (no programming experience required), case studies, and project work. It is suitable for undergraduate students from any faculty who want a grounded understanding of what AI can and cannot do.

Learning objectives

By the end of the course, students will be able to:

  • Explain the main ideas behind contemporary AI systems in plain language.
  • Identify common failure modes of AI systems and the data issues that cause them.
  • Read and assess claims about AI in news articles, product pages and policy documents.
  • Design a small, realistic plan for an AI application, including data needs, evaluation, and a basic harm-mitigation strategy.
  • Reflect critically on ethical, legal and social questions raised by AI deployment.

Course logistics

Prerequisites and software

No formal prerequisites. Familiarity with spreadsheets, basic statistics or introductory programming is useful but not required. We will discuss some simple Python code snippets in class, but no programming experience is needed.

Readings and resources

Readings are short papers, essays and tutorials rather than a single textbook. Links and PDFs will be provided on the course website. Please note that the syllabus may be updated during the semester. For those interested in digging deeper into some of the topics we cover, here are some recommended books and resources:

Books

Online courses

  • AI for everyone by Andrew Ng (Coursera). A non-technical introduction to AI concepts and applications. Free to audit, with a paid certificate option.
  • Elements of AI by the University of Helsinki. A free, friendly introduction to AI and some of its methods.
  • AI essentials by Google (Coursera). This programme is designed for people who want to gain practical AI skills for the workplace with no experience required.
  • Google prompting essentials by Google (Coursera). A short course on how to effectively use and design prompts for large language models.
  • Radical ideas in AI ethics by Pragmatic AI Labs (edX). A course that discusses AI through the lens of human rights and digital autonomy.

Other resources

  • In machines we trust. A podcast series by MIT Technology Review about the promises and perils of AI. Very accessible and engaging.
  • AI for the rest of us. 25-30 minute episodes focused on explaining AI concepts to non-technical listeners.
  • The gradient. A publication that features accessible articles on AI research.
  • AI Now Institute. Research institute focused on the social implications of AI.
  • AI for Good Lab. A Microsoft research group that highlights how AI can change society for the better.
  • The AI incident database. A collection of real-world cases where AI systems have caused harm or failed.
  • Teachable machine. A web-based tool by Google that allows users to create simple machine learning models without coding.
  • The algorithm. A newsletter by MIT Technology Review that covers the latest developments in AI.

Assessments

  • Problem sets (10) — 50%. Short conceptual and practical tasks. Submit Jupyter notebooks (.ipynb), Word documents, or PDFs. Late submissions incur a 10% penalty per day unless authorised in advance. Collaboration for discussion is allowed but answers must be written independently; list collaborators on submission.

  • In-class quizzes (5) — 30%. Each quiz occupies a full lecture. Quizzes are open-book/open-notes and individual assessments. Discussion during quizzes is not permitted.

  • Final group project — 20%. Groups of 3–4 produce a report and a short demo video. The project involves conceptual elements and a harms assessment. More details will be provided in class.

Grading scale

Grade A A- B+ B B- C D F
Range 91–100 86–90 81–85 76–80 71–75 66–70 60–65 <60

Final grades are rounded to the nearest point.

Academic integrity and AI use

Generative AI tools (for example ChatGPT, Claude, GitHub Copilot) may be used as aids for brainstorming or drafting, but all AI use must be declared on submissions. Students must take responsibility for correctness and must attribute any direct text or code produced by AI. The Emory Honour Code applies.

Emory Honour Code

The Emory Undergraduate Academic Honour Code is in effect throughout the semester. The Honour Code applies to any action or inaction that fails to meet the communal expectations of academic integrity. Students should strive to excel in their academic pursuits in a just way with honesty and fairness in mind and avoid all instances of cheating, lying, plagiarizing, or engaging in other acts that violate the Honor Code. Such violations undermine both the individual pursuit of knowledge and the collective trust of the Emory community. Students who violate the Honour Code may be subject to failure of the course, a reportable record, suspension, permanent expulsion, or a combination of these and other sanctions. The Honor Code may be reviewed at: http://catalog.college.emory.edu/academic/policies-regulations/honor-code.html.

Accessibility

Students who require adjustments should contact the Department of Accessibility Services early in the semester and provide documentation. Reasonable accommodations will be made for assessments, including quizzes, according to DAS guidance. Please contact me privately to discuss any specific needs.

Back to top