Wrap Up

Key Takeaways

  • Context is crucial in data science; ignoring it can lead to misleading interpretations and reinforce existing power imbalances.

  • Ethical considerations in data science extend beyond the data itself to how it is framed and communicated, especially in terms of avoiding deficit narratives and being transparent about limitations.

  • Data often comes from environments influenced by power differentials, and understanding this can help in identifying what is missing or misrepresented in a dataset.

  • There are emerging tools and methods for adding context to data, but these need to be more widely adopted and funded to become the norm rather than the exception.


  1. Analyze a dataset of your choice and write a “data biography” for it, answering questions like: Where did it come from? Who collected it? When? How was it collected? Why was it collected?

  2. Find a data visualization online and critique it. Does it consider context? Does it avoid deficit narratives? Is it transparent about its limitations?

  3. Create your own data visualization based on a dataset, making sure to provide context and to consider ethical implications. Write a brief explanation of the choices you made in terms of framing and communication.