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How to Use Data Like You Give a Damn

  • Knack Coworking 2132 3rd Avenue Seattle, WA, 98121 United States (map)

Sometimes we’re presented with numbers and evidence that just don’t seem right. Even when the math is shown to us, the answer doesn’t align with the world we see around us. Rather than burying our heads in the sand and denying the math, or just conceding that our feelings are wrong and accepting the ‘fact’ as presented, we’re developing tools that can ask: “Is there anything that seems objective that might actually be a subjective perspective?”.

Join a presentation with Heather Krause to learn about how data can be used to advance equity. Heather is a data scientist, who over the years, has found her increasingly uncomfortable with the way data is used, taught, collected, and communicated.


Science isn’t always neutral

Inequity and bias in data science are everywhere, and not just in the people who are being hired. It is in the data, the models, and the data viz themselves. Data science gives us the ability to achieve change at an unprecedented scale and pace. But it’s not without problems. The capital-S part of data ‘Science’ offers the impression the numbers we work with are objective; without bias. And data experts’ use of confusing jargon in place of regular, easy to understand phrasing doesn’t help.

Collecting, analyzing and communicating data are not neutral activities. The process of creating evidence is very much dependent on the world view and cultural values of the people involved - from those designing the data collection, those doing the analysis, and those being studied. Even such an apparently simple mathematical process such as taking an average is highly influenced by your point of view. Let’s talk about tools and processes we can use to help surface and correct bias, sexism, racism, colonialism, and other types of latent discrimination in the way we collect data, analyze data, and communicate about data.

About Heather Krause

Sometimes we’re presented with numbers and evidence that just don’t seem right. Even when the math is shown to us, the answer doesn’t align with the world we see around us. We’re developing tools that can ask: “Is there anything that seems objective that might actually be a subjective perspective?”.

I’ve been working as a data scientist for decades. And over the years, I’ve found myself increasingly uncomfortable with the way data is used, taught, collected, and communicated. Inequity and bias in data science are everywhere. And I don’t just mean in the people who are being hired. I mean in the data, the models, and the data viz themselves.