M. Elizabeth Karns is a lawyer and epidemiologist. Her teaching is directly connected to her practice, which has focused on statistical evidence, occupational illnesses, assault and harassment injuries, and reproductive harms. Professor Karns has been involved in every aspect of the data life cycle, from initial design to post-event forensic evaluations. Her work on data science ethics brings together professional practice norms, individual values, and organizational goals. Professor Karns is currently developing a virtual reality experience based on the unintended consequences of automated fraud detection.
Integrating Virtue Ethics into Data Science PracticeCornell Course
Integrating Virtue Ethics into Data Science Practice ()
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Course Overview
What can you do to cultivate the right ethical choices? This course addresses this question by delving into the concept of “virtue ethics” and how it is applied in practical situations. You will explore how the virtue ethics framework relates to existing principles, practices, and codes of conduct in data science as well as how it can be used to inform decisions. First, you will develop the skill to analyze your own habits so they support your beliefs about your ethical character. You will then apply these concepts to a work setting, recognizing the guidelines that exist for your professional practice. Finally, you will discover how to navigate common situations where ethics are at odds with your professional goals or client needs, including situations when organizational ethics and individual ethics are unaligned. By integrating these concepts in practical ways, you will be set up with the tools and practices needed for success in your role and beyond.
Key Course Takeaways
- Develop the skills to analyze your own habits so they support your beliefs about your character
- Recognize the guidelines that exist for your professional practice
- Deploy skills to cope with the absence of a single client in data science projects
- Identify responsible practices within an organization
- Navigate situations where organizational ethics and individual ethics are unaligned
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Course Length
2 weeks
Effort
3 to 5 hours of study per week
Format
100% online, instructor-led
Course Author
Senior Lecturer, Social Statistics, Cornell ILR School
Who Should Enroll
- Data and machine learning scientists
- Data bank managers and curators
- Model and algorithm developers
- Data protection managers and privacy stewards
- Ethics officers
- Statisticians
- IT managers and technology consultants
- Data and business analysts
- Software developers and programmers
- Quality and security engineers
- Compliance professionals
- Lawyers
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