Data Art Manifesto
Sometimes we reach the limit of what is known — and when we go beyond that, it’s art.
Art in various mediums should be used to enhance, challenge and investigate decisions made via data science, artificial intelligence and machine learning.
There are a lot of disciplines that are a delicate balance between art and science. Cooking is a great example — you need to learn the rules, but after that it’s an art to interpret memories into something that can be consumed. With perfumery, chemists are always on the search for new molecules but the emotions evoked with smell are subjective and varied. You can analyze the stock market to death, but there aren’t guarantees. Should falling in love be boiled down to a scientific process? Is raising a child an algorithm?
1. Abstraction of personal information can lead to moving insight
In this example Arlene Birt and Marianne Rosolen represent the top 5 most common ways to die as fillings. How are you going to die?
This is better than a bar chart.
2. Real people with emotions and feelings are behind the data
Many people have seen the movie Waiting for Superman, but the short film “The Inconvenient Truth Behind Waiting for Superman” adds many shades of doubt on testing and data. Should data alone be used to decide who receives a good education, what schools close, how to judge a population of youth?
3. Welcome ambiguity, include outliers in decision or output
Categories, hash tags, groups — there are many ways to gather data together and try to find it. Data science and the concept of what data is personal has important implications for our society but it’s not perfect. Maybe it never should be, to always keep an element of awareness in our decisions and not lazily default to what an organization or corporation creates.
If we allow for too much perfection and analysis, we’ll all wind up looking alike. https://www.wsj.com/articles/i-love-my-unique-personalized-stitch-fix-shirtoh-you-have-one-too-1523027667
4. Science is subject to fallacious interpretation and misinterpretation
In a separate post, This is Not Me: Machines Evaluate My Facebook Posts, I give some examples of how tone analyzers can mistakenly evaluate my Facebook comments.
Heather Krause talks about Ecological Fallacy and using data correctly. It can be tricky, and it’s often tempting to use data incorrectly to tell the story you want to tell.
“Basically, it’s all about trying to answer a different question than the one your data can answer. It’s making assumptions about individuals in a group based your knowledge of that group as a whole. It can be tempting to assume we understand what’s happening with individuals within a group, but it’s important to resist the urge.”
So there’s more to come
These four points are a just a starting point for exploring information in new and different ways. While algorithms, visualizations and journalism can reveal stories and data in various and emotional ways, it’s important to address the unknown and mysterious aspects of our world.