Visualization Group News Today

Umwelt is a new a system that enables blind and low-vision users to author accessible, interactive charts representing data in three modalities: visualization, textual description, and sonification.

After finally removing the last two fasteners from the canister and nearly 4 months after it was dropped by OSIRIS-REx in the Utah desert, the curation

Open-source software written by MIT Morningside Academy of Design Fellow Jonathan Zong and others in the MIT Visualization Group improves accessibility of online graphics for blind users.

A new tool helps scientists develop machine-learning models that generate richer, more detailed captions for charts, and vary the level of complexity of a caption based on the needs of users. This could help improve accessibility for people with visual disabilities.

Saliency cards, developed at MIT and IBM, seek to describe a machine-learning model’s behavior and to help users quickly compare methods so they can choose the right one for their task.

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Researchers have developed prototypes of data visualizations that enable blind and low-vision people who use screen readers to more easily explore information and gain deeper insights about chart data. They plan to turn these prototypes into a software tool that could help designers make data visualizations accessible and navigable.

MIT researchers developed a method that helps a user to better understand a machine-learning model’s reasoning, and how that reasoning compares to that of a human. The technique enables a user to rapidly identify and analyze patterns in a model’s behavior.

In a busy hospital, a radiologist is using an artificial intelligence system to help her diagnose medical conditions based on patients' X-ray images. Using the AI system can help her make faster diagnoses, but how does she know when to trust the AI's predictions?

MIT researchers created a method that helps humans develop a more accurate mental model of an artificial intelligence teammate, so they have a better understanding of when they should trust the AI agent’s algorithmic predictions.