Exhausting. While this might be a cause for celebration for some, my preliminary audit shows that this is yet again another tool for easily creating inaccessible data experiences.
No SR or keyboard access, no semantic control of marks or their relationship to one another, etc
As just naked DOM stuff, this means that yet again the onus is not on the creator of the library but the consumer to do accessibility. Why do we continue to make it easy to make inaccessible things?
Disappointed because this solves technical barriers for some, but produces many.
Why do big names/groups/companies in this space continue to innovate exclusionary tools, libraries, and resources?
These fast and easy solutions create more accessibility problems than they solve. We are long overdue for accessibility and inclusion in the wide field of data.
After hearing I do accessibility in data science, it is always weird when a researcher or data practitioner says, "how interesting, very cool work."
As if human rights is some kind of curious little subject they hadn't considered? This is projected by law?? They need to do it??
Designers and web engineers tend to know this is important, so the comments are rarely off-putting after I give a talk. They usually attend because they need the skills.
From them I often get, "wow, this is exactly what I was looking for and knew I needed! Thank you!"
But many academics and analysts are so used to compartmentalizing info and literally deleting human consideration from their work that they do not know they are neglecting significant legal precedence.
"Oh how curious that someone would need this 'access' you speak of. Strange!"
Unlearning ableism also includes unlearning self-deprecation.
I used to really loathe myself, but trying to come up with words and terms that weren't ableist made me realize that I actually did not know myself very well at all.
I would catch myself wanting to insult myself after a mistake. The only reasonable thing I could replace an ableist slur with was the truth (which is frustratingly unsatisfying).
*ableist slur towards myself*
Which was replaced by
"I hate myself for messing up because I am not good enough" (still ableist)
Then replaced by
"I am mad at myself for making a mistake and I don't like how it feels to make mistakes" (still not good)
1. Commissioned Artist (traditional, dry medium) 2. Front desk at a toy store 3. Camp counselor 4. Night security 5. Dog groomer 6. Furniture mover/assembler
Omg bonus-bonus round:
1. Student body president in college 2. Community organizer 3. Fundraiser 4. Dorm RA 5. Volunteer for kids after school 6. Student paper editor
Data visualization cares disproportionately far too much about designing for colorblindness relative to other disabilities that are more common (visual impairments included).
(A thread on disability, race, and patriarchy in data visualization.)
~4.5% of people with northern European ancestry are colorblind. But less than half of a percent of women are.
This means that nearly 8% of men from a northern European background have some form of colorblindness.
*Colorblindness affects WHITE MEN the most.*
Why does this matter?
Because designers, scientists, and engineers in our field continue to produce palettes, guides, research, and tools for dealing with colorblindness when visualizing data.
But where are tools and resources for all the other kinds of disabilities out there?!