Research that crosses the normal boundaries of educational disciplines, and bounds between academia, industry, and government, is increasingly widespread, and has sometimes led to the spawning of serious recent disciplines. But Munther Dahleh, a professor of electrical engineering and computer science at MIT, says that such multidisciplinary and interdisciplinary work often suffers from numerous shortcomings and handicaps in comparison with more traditionally focused disciplinary work.
But increasingly, he says, the profound challenges that face us in the fashionable world — including climate change, biodiversity loss, how one can control and regulate artificial intelligence systems, and the identification and control of pandemics — require such meshing of experience from very different areas, including engineering, policy, economics, and data evaluation. That realization is what guided him, a decade ago, within the creation of MIT’s pioneering Institute for Data, Systems and Society (IDSS), aiming to foster a more deeply integrated and lasting set of collaborations than the same old temporary and ad hoc associations that occur for such work.
Dahleh has now written a book detailing the strategy of analyzing the landscape of existing disciplinary divisions at MIT and conceiving of a option to create a structure geared toward breaking down a few of those barriers in a long-lasting and meaningful way, so as to bring about this recent institute. The book, “Data, Systems, and Society: Harnessing AI for Societal Good,” was published this March by Cambridge University Press.
The book, Dahleh says, is his attempt “to explain our pondering that led us to the vision of the institute. What was the driving vision behind it?” It’s geared toward numerous different audiences, he says, but particularly, “I’m targeting students who’re coming to do research that they need to handle societal challenges of differing kinds, but utilizing AI and data science. How should they be occupied with these problems?”
A key concept that has guided the structure of the institute is something he refers to as “the triangle.” This refers back to the interaction of three components: physical systems, people interacting with those physical systems, after which regulation and policy regarding those systems. Each of those affects, and is affected by, the others in various ways, he explains. “You get a posh interaction amongst these three components, after which there may be data on all these pieces. Data is form of like a circle that sits in the course of this triangle and connects all these pieces,” he says.
When tackling any big, complex problem, he suggests, it is beneficial to think when it comes to this triangle. “Should you’re tackling a societal problem, it’s very necessary to grasp the impact of your solution on society, on the people, and the role of individuals within the success of your system,” he says. Often, he says, “solutions and technology have actually marginalized certain groups of individuals and have ignored them. So the massive message is at all times to think in regards to the interaction between these components as you consider how one can solve problems.”
As a selected example, he cites the Covid-19 pandemic. That was an ideal example of a giant societal problem, he says, and illustrates the three sides of the triangle: there’s the biology, which was little understood at first and was subject to intensive research efforts; there was the contagion effect, having to do with social behavior and interactions amongst people; and there was the decision-making by political leaders and institutions, when it comes to shutting down schools and firms or requiring masks, and so forth. “The complex problem we faced was the interaction of all these components happening in real-time, when the information wasn’t all available,” he says.
Making a call, for instance shutting schools or businesses, based on controlling the spread of the disease, had immediate effects on economics and social well-being and health and education, “so we needed to weigh all this stuff back into the formula,” he says. “The triangle got here alive for us in the course of the pandemic.” Because of this, IDSS “became a convening place, partly due to all the several facets of the issue that we were excited about.”
Examples of such interactions abound, he says. Social media and e-commerce platforms are one other case of “systems built for people, they usually have a regulation aspect, they usually fit into the identical story if you happen to’re trying to grasp misinformation or the monitoring of misinformation.”
The book presents many examples of ethical issues in AI, stressing that they have to be handled with great care. He cites self-driving cars for example, where programming decisions in dangerous situations can appear ethical but result in negative economic and humanitarian outcomes. For example, while most Americans support the concept a automotive should sacrifice its driver somewhat than kill an innocent person, they wouldn’t buy such a automotive. This reluctance lowers adoption rates and ultimately increases casualties.
Within the book, he explains the difference, as he sees it, between the concept of “transdisciplinary” versus typical cross-disciplinary or interdisciplinary research. “All of them have different roles, they usually have been successful in other ways,” he says. The secret’s that the majority such efforts are likely to be transitory, and that may limit their societal impact. The actual fact is that even when people from different departments work together on projects, they lack a structure of shared journals, conferences, common spaces and infrastructure, and a way of community. Creating an instructional entity in the shape of IDSS that explicitly crosses these boundaries in a set and lasting way was an attempt to handle that lack. “It was primarily about making a culture for people to take into consideration all these components at the identical time.”
He hastens so as to add that after all such interactions were already happening at MIT, “but we didn’t have one place where all the scholars are all interacting with all of those principles at the identical time.” Within the IDSS doctoral program, for example, there are 12 required core courses — half of them from statistics and optimization theory and computation, and half from the social sciences and humanities.
Dahleh stepped down from the leadership of IDSS two years ago to return to teaching and to proceed his research. But as he reflected on the work of that institute and his role in bringing it into being, he realized that unlike his own academic research, during which every step along the best way is rigorously documented in published papers, “I haven’t left a trail” to document the creation of the institute and the pondering behind it. “No one knows what we thought of, how we thought of it, how we built it.” Now, with this book, they do.
The book, he says, is “sort of leading people into how all of this got here together, in hindsight. I need to have people read this and form of understand it from a historical perspective, how something like this happened, and I did my best to make it as comprehensible and easy as I could.”
