[liveblog] metaLab
Harvard metaLab is giving an informal Berkman Klein talk about their work on designing for ethical AI. Jeffrey Schnapp introduces metaLab as “an idea foundry, a knowledge-design lab, and a production studio experimenting in the networked arts and humanities.” The discussion today will be about metaLab’s various involvements in the Berkman Klein – MIT MediaLab project on ethics and governance of AI. The conference is packed with Fellows and the newly-arrived summer interns.
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NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people. |
Matthew Battles and Jessica Yurkofsky begin by talking about Curricle, a “new platform for experimenting with shopping for courses.” How can the experience be richer, more visual, and use more of the information and data that Harvard has? They’ve come up with a UI that has three elements: traditional search, a visualization, and a list of the results.
“They’ve been grappling with the ethics of putting forward new search algorithms. ”They’ve been grappling with the ethics of putting forward new search algorithms. The design is guided by transparency, autonomy, and visualization. Transparency means that they make apparent how the search works, allowing students to assign weights to keywords. If Curricle makes recommendations, it will explain that it’s because other students like you have chosen it or because students like you have never done this, etc. Visualization shows students what’s being returned by their search and how it’s distributed.
Similar principles guide a new project, AI Compass, that is the entry point for information about Berkman Klein’s work on the Ethics and Governance of AI project. It is designed to document the research being done and to provide a tool for surveying the field more broadly. They looked at how neural nets are visualized, how training sets are presented, and other visual metaphors. They are trying to find a way to present these resources in their connections. They have decided to use Conway’s Game of Life [which I was writing about an hour ago, which freaks me out a bit]. The game allows complex structures to emerge from simple rules. AI Compass is using animated cellular automata as icons on the site.
metaLab wants to enable people to explore the information at three different scales. The macro scale shows all of the content arranged into thematic areas. This lets you see connections among the pieces. The middle scale shows the content with more information. At the lowest scale, you see the resource information itself, as well as connections to related content.
Sarah Newman talks about how AI is viewed in popular culture: the Matrix, Ahnuld, etc. “We generally don’t think about AI as it’s expressed in the tools we actually use”We generally don’t think about AI as it’s expressed in the tools we actually use, such as face recognition, search, recommendations, etc. metaLab is interested in how art can draw out the social and cultural dimensions of AI. “What can we learn about ourselves by how we interact with, tell stories about, and project logic, intelligence, and sentience onto machines?” The aim is to “provoke meaningful reflection.”
One project is called “The Future of Secrets.” Where our email and texts be in 100 years? And what does this tell us about our relationship with our tech. Why and how do we trust them? It’s an installation that’s been at the Museum of Fine Arts in Boston and recently in Berlin. People enter secrets that are printed out anonymously. People created stories, most of which weren’t true, often about the logic of the machine. People tended to project much more intelligence on the machine than was there. Cameras were watching and would occasionally print out images from the show itself.
From this came a new piece (done with fellow Rachel Kalmar) in which a computer reads the secrets out loud. It will be installed at the Berkman Klein Center soon.
Working with Kim Albrecht in Berlin, the center is creating data visualizations based on the data that a mobile phone collects, including the accelerometer. “These visualizations let us see how the device is constructing an image of the world we’re moving through”These visualizations let us see how the device is constructing an image of the world we’re moving through. That image is messy, noisy.
The lab is also collaborating on a Berlin exhibition, adding provocative framing using X degrees of Separation. It finds relationships among objects from disparate cultures. What relationships do algorithms find? How does that compare with how humans do it? What can we learn?
Starting in the fall, Jeffrey and a co-teacher are going to be leading a robotics design studio, experimenting with interior and exterior architecture in which robotic agents are copresent with human actors. This is already happening, raising regulatory and urban planning challenges. The studio will also take seriously machine vision as a way of generating new ways of thinking about mobility within city spaces.
Q&A
Q: me: For AI Compass, where’s the info coming from? How is the data represented? Open API?
Matthew: It’s designed to focus on particular topics. E.g., Youth, Governance, Art. Each has a curator. The goal is not to map the entire space. It will be a growing resource. An open API is not yet on the radar, but it wouldn’t be difficult to do.
Q: At the AI Advance, Jonathan Zittrain said that organizations are a type of AI: governed by a set of rules, they grow and learn beyond their individuals, etc.
Matthew: We hope to deal with this very capacious approach to AI is through artists. What have artists done that bear on AI beyond the cinematic tropes? There’s a rich discourse about this. We want to be in dialogue with all sorts of people about this.
Q: About Curricle: Are you integrating Q results [student responses to classes], etc.?
Sarah: Not yet. There’s mixed feeling from administrators about using that data. We want Curricle to encourage people to take new paths. The Q data tends to encourage people down old paths. Curricle will let students annotate their own paths and share it.
Jeffrey: We’re aiming at creating a curiosity engine. We’re working with a century of curricular data. This is a rare privilege.
me: It’d enrich the library if the data about resources was hooked into LibraryCloud.
Q: kendra: A useful feature would be finding a random course that fits into your schedule.
A: In the works.
Q: It’d be great to have transparency around the suggestions of unexpected courses. We don’t want people to be choosing courses simply to be unique.
A: Good point.
A: The same tool that lets you diversify your courses also lets you concentrate all of them into two days in classrooms near your dorm. Because the data includes courses from all the faculty, being unique is actually easy. The challenge is suggesting uniqueness that means something.
Q: People choose courses in part based on who else is choosing that course. It’d be great to have friends in the platform.
A: Great idea.
Q: How do you educate the people using the platform? How do you present and explain the options? How are you going to work with advisors?
A: Important concerns at the core of what we’re thinking about and working on.
