RESEARCH PRACTICA

Research Practicum seminars will be offered every Fall and Spring Semester during the course of the project as 0-3 credit special topics courses (that can be retaken for credit). As an incoming trainee, you will start by enrolling in the Fall Research Practicum, where you will gain basic knowledge about current topics related to bias in data, humans, and institutions. The following Spring Semester you will conduct research with faculty and trainees from other disciplines, engage in collaboration, and present your work. These are expected to be collaborative and supportive settings, where trainees and faculty alike will have the opportunity to learn from one another.

Fellows (NRT-funded trainees) are required to participate in Research Practicum meetings for the entirety of their funding period (up to 2 years/4 semesters). Non-funded trainees are required to enroll in at least 2 seminars, but expected to remain active participants. 

All new-incoming trainees must enroll in the Fall Research Practicum of their application year.

• Bias in DATA

  Trainees will learn about and identify relevant biases in the forms of data they use in their own research; they will also identify potential biases in each other's research and methodology. They will conduct projects with a goal of publishing their work. Topics may include: sampling bias, historical bias in datasets for machine learning, bias in hypothesis-testing, and how biased datasets can propagate unfair and undesirable outcomes in automated decision-making.  

   

• Bias in HUMANS

  Trainees will learn about anti-racism and practice techniques for identifying and addressing bias in themselves and in their own research. Topics may include: implicit bias, stereotyping and prejudice, heuristics that lead to biases in human judgments and decision-making, linguistic biases, and how to define, identify, and handle microaggressions. 

   

• Bias in INSTITUTIONS

  Trainees will select several social/institutional domains in which to identify and document bias. Topics may include: structural racism, biased institutional practices, bias expressed in social networks, partisan conflict and disagreement in the media, and ways of remedying bias via education and policy.