STAT 390
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Data Science Ethics
Course Description
Ethical reasoning in data science, navigating complex challenges as disciples in the discipline, integrating fairness, integrity, and respect for individuals across the data science pipeline, making positive impact within statistical, actuarial, and data science communities.
When Taught
Fall and Winter
Min
1.5
Fixed/Max
1.5
Fixed
1.5
Fixed
0
Recommended
Stat 340 or concurrent enrollment.
Title
Ethical Frameworks
Learning Outcome
Students will recognize basic ethical theories and identify their advantages and limitations.
Title
Data Privacy
Learning Outcome
Students will recognize: The need for protecting data, the types of data that need to be protected, and methods for effective data privacy.
Title
Data Collection
Learning Outcome
Students will be able to identify customs, laws, and regulations that pertain to ethical data collection and to properly use statistical and ethical reasoning when planning studies.
Title
Communication of Statistical Results
Learning Outcome
Students will be able to identify and address common threats to ethical analyses, and will be able to communicate crucial aspects of data analyses which would enable and encourage ethical use of the results.
Title
Modeling & Algorithmic Bias
Learning Outcome
Given a model, students will be able to identify possible adverse effects to sub-groups in a population and suggest actions to mitigate these negative effects. Additionally, students will be able to recognize hidden model biases and identify how bias propagation can occur in models.
Title
Modeling
Learning Outcome
Students will be able to explain the importance of modeling transparency, reproducibility of results, and the lifecycle of a statistical model. Students will also be able to explain the primary concepts in the ethical development and use of artificial intelligence (AI) models.
Title
Case Studies
Learning Outcome
Students will be able to develop ethical solutions to ethically challenging situations.
