Researchers operate inside of a society with norms, values, and expectations
To behave ethically is to behave in a way that is considered socially responsible
Human subjects
Operates on principles of non-maleficence, beneficence, autonomy, and justice
Requires informed consent of participants
Uses anonymity and confidentiality to protect identities
Animal subjects
Operates on principles of non-maleficence, beneficence, and justice
Where possible, researchers must aim for replacement, reduction, and refinement
University of Utah
Data science makes use data that is available from a variety of sources, often in ways that are not directly connected to the original data collection process
At the same time, there are limited oversights governing the reuse of data by researchers or others.
Non-maleficence: Could the use of this data be harmful?
Beneficence: How will the use of this data be beneficial?
Autonomy: Did stakeholders contribute this data willingly?
Justice: Would use of this data propagate inequities?
How were the data obtained?
For whom, or for what purpose, were the data obtained?
Would stakeholders be comfortable if they knew the data were being collected, stored or shared?
The 2014 Facebook Social Contagion Study
Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. "Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks." Proceedings of the National Academy of Sciences 111 (24): 8788–90. https://doi.org/10.1073/pnas.1320040111.
How might the data be biased?
How might the data be manipulated to bias results?
How might data be used to promote existing biases?
Towards Data Science
Take a moment and go over one or more of your datasets from your project to address the following questions:
What do you know about where your data comes from? Where would you find out?
What are some potential sources of bias in your data?
Are there any ways your use of this data cause harm or propagate biases?
With one of your neighbors, discuss your project in terms of ethical principles of non-maleficence, beneficence, autonomy, and justice.
Non-maleficence: Could the use of this data be harmful?
Beneficence: How will the use of this data be beneficial?
Autonomy: Did stakeholders contribute this data willingly?
Justice: Would use of this data propagate inequities?
Where do we draw the line between narrative and agenda?
National Public Radio
Linear modeling in R
Moving forward with the final project
Postering 101