Data Hazards#
Prerequisites#
Prerequisite |
Importance |
Notes |
|---|---|---|
Helpful |
Reading the self-reflection chapter will give you a good starting point to reflect on your work before applying the Data Hazards labels. This chapter gives more context as to why self-reflection can be a useful tool in your research. |
Summary#
This chapter will discuss the Data Hazards Project, which is a community-developed shared vocabulary of data science risks. The vocabulary represents data ethics concepts in the form of Data Hazard labels (which resemble chemical hazard labels). These are provided alongside materials to help data practitioners use them, such as templates for workshops or self-reflection.
These labels and materials exist to facilitate interdisciplinary discussions and self-reflection about all kinds of data ethics risks. Ultimately, the project aims to help data practitioners to identify and mitigate these risks, by removing barriers for researchers to engage with these practices. The chapter describes the motivation behind the project, how you can use it to support your data-intensive work, and how you can contribute to it.
Motivation#
Mitigate risks in your data science work: we all (hopefully!) want our data science work to do good, but as data scientists we are often trained primarily in solving technical problems, rather than ethical ones. Data Hazards should help you to identify risks that you might not have considered and ways of mitigating those risks.
Change research culture: contribute to the wider adoption of data scientists considering the broader ethical implications of their work.