Best Practices in Ethical Data Collection
Towards Identity & Accountability
Foto: Yezhang Wang
It seems to have become tradition to confront the ethics of data science mostly through reflection on controversy and righting past wrongs. While this might lead to greater awareness and continual growth in the right direction is certainly to be applauded, the question still lingers whether or not this approach is enough in a field that changes so rapidly from day to day and year to year. Framed in the major philosophical schools of ethics, from consequentialism to virtue ethics, this talk walks through the best practices in ethical data collection that not only lead to more ethical project management, but also guide institutions and individuals towards lasting and preemptive change in the face of a field without clear role models.
The talk outlines the many aspects as:
- Catch ethical issues early through data sheets.
- Create ethics codes to communicate values with your team.
- Treat ethics as a central part of your workflow.
- Practice data ethics like learning a new instrument.
- Engage in open source and open data, join Data for Good projects.
- Find and imitate moral exemplars in your field.
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