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We had a great chat with Heather Krause, Founder of We All Count Project for Data Equity. Heather is a statistician by training and is presently using her skills and passion to improve equity in data science. She practiced data science in an international setting for many years before realizing many of her own practices were problematic, both reflecting and reinforcing western ways of thinking and often de-legitimizing the values of the people and groups she was studying. Rather than blow up her career, Heather thankfully imagined a new way of doing data science that would marry rigor with equity in the planning, execution, and communication of quantitative research. She now works with a range of clients from the public and nonprofit sectors, as well as academia and other research institutions, to help them identify areas for improvement and tangible practices to improve their own research.
We talk with Heather about what data equity means (and, for that matter, what data science means), the many hats she wears not only as a data scientist herself but as a therapist for researchers who are learning that their long-held practices and traditions may be inequitable in both obvious and not-so-obvious ways, and her philosophy that there is no trade off between rigor and equity in data science (and in fact they should be mutually reinforcing!). It was a great conversation, and we know our listeners will appreciate Heather's candor and thoughtfulness.
Friends, Heather brings The Goods. First of all, bless her soul, she's a disciplined blogger. Her website is also chock full of tools and resources which you can use to inform your approach to data equity. If you're just now starting to think about approaching your work through the lens of equity, we suggest you start with these resources. The website also offers a Data Equity Framework which researchers can use to think through some of the thorny issues that arise when planning a study, collecting data, analyzing data, and reporting on results. Check out the tools and framework, and if you're interested in digging deeper, please reach out to Heather for more information!
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We had a great chat with Heather Krause, Founder of We All Count Project for Data Equity. Heather is a statistician by training and is presently using her skills and passion to improve equity in data science. She practiced data science in an international setting for many years before realizing many of her own practices were problematic, both reflecting and reinforcing western ways of thinking and often de-legitimizing the values of the people and groups she was studying. Rather than blow up her career, Heather thankfully imagined a new way of doing data science that would marry rigor with equity in the planning, execution, and communication of quantitative research. She now works with a range of clients from the public and nonprofit sectors, as well as academia and other research institutions, to help them identify areas for improvement and tangible practices to improve their own research.
We talk with Heather about what data equity means (and, for that matter, what data science means), the many hats she wears not only as a data scientist herself but as a therapist for researchers who are learning that their long-held practices and traditions may be inequitable in both obvious and not-so-obvious ways, and her philosophy that there is no trade off between rigor and equity in data science (and in fact they should be mutually reinforcing!). It was a great conversation, and we know our listeners will appreciate Heather's candor and thoughtfulness.
Friends, Heather brings The Goods. First of all, bless her soul, she's a disciplined blogger. Her website is also chock full of tools and resources which you can use to inform your approach to data equity. If you're just now starting to think about approaching your work through the lens of equity, we suggest you start with these resources. The website also offers a Data Equity Framework which researchers can use to think through some of the thorny issues that arise when planning a study, collecting data, analyzing data, and reporting on results. Check out the tools and framework, and if you're interested in digging deeper, please reach out to Heather for more information!