CPS 273: Teaching & Learning Machine Ethics
Databases
The following databases are used most often by students studying Mathematics. Use them to search for high-quality research related to your topic. MathSciNet is the best-bet for mathematics research.
- ACM Digital Library (Association for Computing Machinery) This link opens in a new windowThe most comprehensive collection of full-text articles and bibliographic records in existence covering the fields of computing and information technology. Includes the complete collection of ACM's publications, including journals, conference proceedings, magazines, newsletters, and multimedia titles (excludes ACM books). The ACM Digital Library is heavily integrated with and includes unrestricted access to the Guide to Computing Literature bibliography.
- General Science Full Text (H.W. Wilson) This link opens in a new windowAn index and full text database featuring subjects such as astronomy, biology, botany, chemistry, conservation, health & medicine, oceanography, physics, zoology and more.
Indexing from 1984 to present, full text from 1995 to present. - JStor This link opens in a new windowA collection of full-text articles from over 3000 scholarly journals, many dating from the nineteenth-century to the last 4 or 5 years. A growing number of titles are available through the current issues, as part of their "Current Scholarship Program". Select Advanced Search to strategically search amongst 45 disciplines. Choose PDF from within JStor to properly view and print articles. For convenience, JStor is now searchable on Facebook.
- MathSciNet This link opens in a new windowEncompassing both Mathematical Reviews and Current Mathematical Publications, this index is the most comprehensive in the discipline. Covers 1940 to the present.
- Project Muse This link opens in a new windowProvides access to scholarly journals and eBooks in the arts, humanities, and social sciences. Dates of coverage varies per title.
- Science Citation Index (Web of Science) This link opens in a new windowIndex to content from core journals in all areas of science and technology. Provides lists of sources cited, plus publications citing the original article. Also known as SCI. Covers 1900 to the present.
Areas of Interest and Ethical Concerns
- AI/ML
- User Interfaces
- Internet
- Social Media
- Resources (e.g., minerals, water, electricity)
- Human Labor (e.g., replacement of human labor, hidden laborers)
- Cognitive transformations as languaging beings / linguistic autonomy ● Privacy
- Misinformation
- Fairness/Bias
- Accessibility
- Accountability and Responsibility
- Transparency and Explainability
Topic Description
This project looks at algorithmic bias in model prediction and its ethical implications. There are several components to this:
- Use appropriate data to build a very simple model (even just a mean!) to predict a future outcome.
- Use inappropriate data to build a model.
- Quantify and compare efficacy of models.
- Discuss small scale ethical implications, negative outcomes for decision making.
- Explore broader implications through classroom discussion of algorithmic bias reading to learn about
- How bias is IMPOSSIBLE to remove (even if just considering sampling error).
- How bias is perpetuated
- Strategies to reduce bias? Data cards? Detailed understanding of collection methods?
Course Description
The course introduces students to the foundations of data science. The examined topics include getting, wrangling, exploring, visualizing and drawing conclusions from the data. Data ethics issues are also addressed. Data sets and examples are drawn from a wide variety of fields. The statistical program R is extensively used in this course to carry out all aspects of work with data.