Kestrell

Researchers have created prototypes that enable screen-reader users to quickly and easily navigate through multiple levels of information in an online chart.
Adam Zewe | MIT News Office
June 2, 2022

https://news.mit.edu/2022/data-visualization-accessible-blind-0602

Data visualizations on the web are largely inaccessible for blind and low-vision individuals who use screen readers, an assistive technology that reads on-screen elements as text-to-speech. This excludes millions of people from the opportunity to probe and interpret insights that are often presented through charts, such as election results, health statistics, and economic indicators.

When a designer attempts to make a visualization accessible, best practices call for including a few sentences of text that describe the chart and a link to the underlying data table — a far cry from the rich reading experience available to sighted users.

An interdisciplinary team of researchers from MIT and elsewhere is striving to create screen-reader-friendly data visualizations that offer a similarly rich experience. They prototyped several visualization structures that provide text descriptions at varying levels of detail, enabling a screen-reader user to drill down from high-level data to more detailed information using just a few keystrokes.

The MIT team embarked on an iterative co-design process with collaborator Daniel Hajas, a researcher at University College London who works with the Global Disability Innovation Hub and lost his sight at age 16. They collaborated to develop prototypes and ran a detailed user study with blind and low-vision individuals to gather feedback.

....The researchers defined three design dimensions as key to making accessible visualizations: structure, navigation, and description. Structure involves arranging the information into a hierarchy. Navigation refers to how the user moves through different levels of detail. Description is how the information is spoken, including how much information is conveyed.

Using these design dimensions, they developed several visualization prototypes that emphasized ease-of-navigation for screen-reader users. One prototype, known as multiview, enabled individuals to use the up and down arrows to navigate between different levels of information (like the chart title as the top level, the legend as the second level, etc.), and the right and left arrow keys to cycle through information on the same level (such as adjacent scatterplots). Another prototype, known as target, included the same arrow key navigation but also a drop-down menu of key chart locations so the user could quickly jump to an area of interest.

“Our goal is not just to work within existing standards to make them serviceable. We really set out to do grounded speculation and imagine where we can push what is possible with these existing standards. We didn’t want to limit ourselves to refitting tools that were designed for images,” says Zong.

Kes: I'm currently looking through some old directories in order to put together a resume on previous projects I have worked on, and I came across this old LJ post, and even I am thought, WTF?? That is one crazy idea, Kes! But also: I think we have the technology (exccept for flash, I think we vanquished that evil(...So here is the rest of the post.

This evening I will be attending an event at MIT titled
" It's a Small World: How Virtual Communities Are Changing the Ways We Relate"
(6-8:30 p.m. at the MIT Campus Broad Institute NE 30, corner of Main Street and Ames Street).

The registration Web site mentioned homework, so, as one of the discussion topics will be "What Kind of World Would You Make: Second Life as Thought Experiment," I decided to do the Hermione thing and plan my corner of the virtual world.

*The Jorge Luis Borges Book Center and Dog Park*
with explanations about accessibility and how a visually-impaired user accesses a visual interface

( continued below cut )

Kes: I totally fangirl for Hugh Herr and, if you are interested in prosthetics, I encourage you to go read the rest of this article, and then go read more about Hugh Herr's work.

excerpt

Interdisciplinary research center funded by philanthropist Lisa Yang aims to mitigate disability through technologies that marry human physiology with electromechanics.
September 23, 2021
https://news.mit.edu/2021/new-bionics-center-established-mit-24-million-gift-0923

With the establishment of the new K. Lisa Yang Center for Bionics, MIT is pushing forward the development and deployment of enabling technologies that communicate directly with the nervous system to mitigate a broad range of disabilities. The center’s scientists, clinicians, and engineers will work together to create, test, and disseminate bionic technologies that integrate with both the body and mind.

The center is funded by a $24 million gift to MIT’s McGovern Institute for Brain Research from philanthropist Lisa Yang, a former investment banker committed to advocacy for individuals with visible and invisible disabilities. Her previous gifts to MIT have also enabled the establishment of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience, Hock E. Tan and K. Lisa Yang Center for Autism Research, Y. Eva Tan Professorship in Neurotechnology, and the endowed K. Lisa Yang Post-Baccalaureate Program.

....To develop prosthetic limbs that move as the brain commands or optical devices that bypass an injured spinal cord to stimulate muscles, bionic developers must integrate knowledge from a diverse array of fields — from robotics and artificial intelligence to surgery, biomechanics, and design. The K. Lisa Yang Center for Bionics will be deeply interdisciplinary, uniting experts from three MIT schools: Science, Engineering, and Architecture and Planning. With clinical and surgical collaborators at Harvard Medical School, the center will ensure that research advances are tested rapidly and reach people in need, including those in traditionally underserved communities.

To support ongoing efforts to move toward a future without disability, the center will also provide four endowed fellowships for MIT graduate students working in bionics or other research areas focused on improving the lives of individuals who experience disability.

The center will be led by Hugh Herr,
https://www.media.mit.edu/people/hherr/overview/
a professor of media arts and sciences at MIT’s Media Lab, and
Ed Boyden,
https://mcgovern.mit.edu/profile/ed-boyden/
the Y. Eva Tan Professor of Neurotechnology at MIT, a professor of biological engineering, brain and cognitive sciences, and media arts and sciences, and an investigator at MIT’s McGovern Institute and the Howard Hughes Medical Institute.
A double amputee himself, Herr is a pioneer in the development of bionic limbs to improve mobility for those with physical disabilities.“The world profoundly needs relief from the disabilities imposed by today’s nonexistent or broken technologies. We must continually strive towards a technological future in which disability is no longer a common life experience,” says Herr. “I am thrilled that the Yang Center for Bionics will help to measurably improve the human experience for so many.”
Boyden, who is a renowned creator of tools to analyze and control the brain, will play a key role in merging bionics technologies with the nervous system. “The Yang Center for Bionics will be a research center unlike any other in the world,” he says. “A deep understanding of complex biological systems, coupled with rapid advances in human-machine bionic interfaces, mean we will soon have the capability to offer entirely new strategies for individuals who experience disability. It is an honor to be part of the center’s founding team.”

From the MIT weekly newsletter:

Back by popular demand, the Department of Biology is again hosting 
7.00 (Covid-19, SARS-CoV-2 and the Pandemic),
https://biology.mit.edu/undergraduate/current-students/subject-offerings/covid-19-sars-cov-2-and-the-pandemic/


a special course on the latest Covid-19 science that is open to all MIT students and to the public at large via live-streamed lectures.
Anthony Fauci, chief medical advisor to President Joe Biden and director of the National Institute of Allergy and Infectious Diseases, 
spoke with the class on Wednesday. 
http://web.mit.edu/webcast/biology/f21/covid-19-sars-cov-2-and-the-pandemic/
In following weeks, professors Facundo Batista and Richard Young will discuss the science of the pandemic with Amy Barczak, Dan Barouch, Arup Chakraborty,
Victoria Clark, Shane Crotty, Britt Glaunsinger, Salim Karim, Shiv Pillai, Rochelle Walensky, Laura Walker, and Andrew Ward. (Bruce Walker spoke last week.)
Learn more
https://biology.mit.edu/undergraduate/current-students/subject-offerings/covid-19-sars-cov-2-and-the-pandemic/

Kes: Purrhaps this explains why 90% of all images get identified as cats.

From MIT Technology Review

April 1, 2021
The 10 most cited AI data sets are riddled with label errors, according to
a new study out of MIT,
https://arxiv.org/pdf/2103.14749.pdf
and it’s distorting our understanding of the field’s progress.

Data sets are the backbone of AI research, but some are more critical than others. There are a core set of them that researchers use to evaluate machine-learning models as a way to track how AI capabilities are advancing over time. One of the best-known is the canonical image-recognition data set ImageNet, which kicked off the modern AI revolution. There’s also MNIST, which compiles images of handwritten numbers between 0 and 9. Other data sets test models trained to recognize audio, text, and hand drawings.

In recent years, studies have found that these data sets can contain serious flaws. ImageNet, for example, contains
racist and sexist labels
https://excavating.ai/
as well as photos of people’s faces obtained without consent.
The latest study now looks at another problem: many of the labels are just flat-out wrong. A mushroom is labeled a spoon, a frog is labeled a cat, and a high note from Ariana Grande is labeled a whistle. The ImageNet test set has an estimated label error rate of 5.8%. Meanwhile, the test set for QuickDraw, a compilation of hand drawings, has an estimated error rate of 10.1%.
How was it measured? Each of the 10 data sets used for evaluating models has a corresponding data set used for training them. The researchers, MIT graduate students Curtis G. Northcutt and Anish Athalye and alum Jonas Mueller, used the training data sets to develop a machine-learning model and then used it to predict the labels in the testing data. If the model disagreed with the original label, the data point was flagged up for manual review. Five human reviewers on Amazon Mechanical Turk were asked to vote on which label—the model’s or the original—they thought was correct. If the majority of the human reviewers agreed with the model, the original label was tallied as an error and then corrected.

Does this matter? Yes. The researchers looked at 34 models whose performance had previously been measured against the ImageNet test set. Then they remeasured each model against the roughly 1,500 examples where the data labels were found to be wrong. They found that the models that didn’t perform so well on the original incorrect labels were some of the best performers after the labels were corrected. In particular, the simpler models seemed to fare better on the corrected data than the more complicated models that are used by tech giants like Google for image recognition and assumed to be the best in the field. In other words, we may have an inflated sense of how great these complicated models are because of flawed testing data.

Now what? Northcutt encourages the AI field to create cleaner data sets for evaluating models and tracking the field’s progress. He also recommends that
researchers improve their data hygiene when working with their own data. Otherwise, he says, “if you have a noisy data set and a bunch of models you’re
trying out, and you’re going to deploy them in the real world,” you could end up selecting the wrong model. To this end, he open-sourced

the code
https://github.com/cgnorthcutt/cleanlab
he used in his study for correcting label errors, which he says is already in use at a few major tech companies.

Properly.

And it's inaccessible.

The story is: I was cruising through the MIT IAP courses, as I do, and I ran across a lunchtime series on communicating better with your audience.

This will add extra irony later.

The registration form involved using Handshake, which requires you to use a university account to create the Handshake account.

Well, I don't have an academic account, so I contacted the office asking for assistance in registering.

The person got back to me asking for my MIT alum account, which I hadn't used in years, so I went to the alum website and requested a password reset, and checked what the email was for my alum account.

Which I sent back to the contact person at MIT.

This morning I got an email that said I should use the MIT alum email to create a Handshake account, and I should also fill out a disability accommodations form for MIT.

Note, still no offer to actually help me register for this course.

So I go back to Handshake, which requires that I fill out a bunch of personal info, including selecting my university and my major. I have to select these from these funky edit combo boxes, which are really finicky with a screen reader. I spent about ten minutes fighting with the first one to get my university entered, but I just can't seem to get the second list of majors to show me any of the items, and attempts to type into the edit field are not accepted.

I am now so annoyed --and I haven't even gotten to filling out the accommodations request form yet-- that any desire to register for this course has now completely evaporated.

I have now spent more time trying to register for this talk than the talk actually takes.

And the whole response of Oh, is something inaccessible? Our response is: fill out more forms!

Note: this talk is sponsored by the Career Advising Professional Development.

The need for open source low-cost ventilators existed even before the Covid-19 pandemic: there are people with disabilities in this country who were already fighting with health insurance companies to have access to the medical equipment they needed, and hospitals, often in rural areas, who lacked this kind of equipment.
https://emergency-vent.mit.edu/

Kes: What is it about small robots and other engineered objects that just makes you love them?
This story
https://scitechdaily.com/mits-mini-satellite-maker/
makes me think of
this post
https://kestrell.dreamwidth.org/361473.html
that I made about a small poetry-writing satellite that shows up in a John Varley story.