Data Hazards is a project about worst-case scenarios of Data Science. You can [view the website here](https://very-good-science.github.io/data-hazards/) for more information. --- Data Scientists are great at selling our work, for example communicating the gains in efficiency and accuracy, but we are less practiced in thinking about the ethical implications of what we do. The ethical implications go beyond most Ethics Review Boards, to questions about the wider societal impact of Data Science and algorithms. Read our preprint here: https://osf.io/hzmyp/ ## Aims We aim to create resources to: 1. Create a shared vocabulary of Data Hazards in the form of Data Hazard Labels. 2. Make ethical and future-thinking more accessible to data scientists, computer scientists and applied mathematicians to apply to their own work. 3. Enable bringing together and respecting diverse and interdisciplinary viewpoints to this work, through workshops or mailing lists. 4. Find out in what circumstances, and for who, these resources work best for.