Rutgers University 3 credits Data Science
This course is designed to use contemporary case studies in algorithmic bias to teach students to identify and deconstruct machine learning (ML) bias. Students will learn how to combine critical reasoning and their understanding of both the modeling process and ML techniques to identify different types of bias, to assess the impact of technical bias on the model (outcome), and discuss the social and economic impact of deploying a biased model. In the second half of the semester, students will apply and critique a statistical-ML solution to mitigate algorithmic bias in a case study dataset.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.