The past, present and future of data analysis

Some interesting reads, courtesy of The Economist’s data analysis newsletter, Off The Charts. Let’s start with this question — are glasses-wearers really less conscientiousness than those who wear a headscarf?

Objective or Biased: On the questionable use of Artificial Intelligence for job applicationsBR24
Software programs promise to identify the personality traits of job candidates based on short videos. With the help of Artificial Intelligence (AI) they are supposed to make the selection process of candidates more objective and faster. An exclusive data analysis shows that an AI scrutinized by BR (Bavarian Broadcasting) journalists can be swayed by appearances. This might perpetuate stereotypes while potentially costing candidates the job.

Here, Stephanie Evergreen makes a solid, essential case for broadening our view of data visualisation and its history. I’ve mentioned khipus here before, but not within this context.

Decolonizing Data VizEvergreen Data
When we talked about these khipu and other forms of indigenous data visualization in a recent panel (with January O’Connor (Tlingit, Kake, Alaska), Mark Parman (Cherokee), & Nicky Bowman (Lunaape/Mohican)), someone in the audience commented, “It made me reflect on traditional Hmong clothing and how my ancestors have embroidered certain motifs on traditional clothing to communicate one’s clanship, what dialect of Hmong one spoke, marital status, everyday life, etc.” And this is one reason why it is so critically important to decolonize data visualization. When white men decide what counts (and doesn’t count) in terms of data, and what counts (and doesn’t count) as data visualization, and what counts (and doesn’t count) as data visualization history, they are actively gaslighting Black and Brown people about their legacy as data visualizers. When we shine a light on indigenous data visualization, we are intentionally saying the circle is much much wider and, as Nicky Bowman said, “There’s room for everyone in the lodge.”

After reconciling the past, let’s look to the future.

Who will shape the future of data visualization? Today’s kids!Nightingale
Graphs are everywhere. So, with the proper instruction, I’d expect today’s kids to become adults that are more proficient at visualizing and interpreting data than today’s adults. Besides parents, teachers, or friends, news organizations also play a role in shaping today’s kids. As Jon pointed out, news organizations can do a great job explaining to us how to read more advanced graphs.

On the other hand, as Sharon and Michael mentioned, because graphs are everywhere, there’s a danger for kids to start thinking that graphs are objective. So it is important for adults to start teaching kids how to think critically, to not necessarily accept the graph and the data at face value. In other words, it’s essential for kids to develop a toolbox. This is good for them and good for democracy — eventually, today’s kids will become more informed citizens.

Something I’m sure Jonathan Schwabish would agree with.