The amount of waste generated by the construction sector underscores an urgent need for embracing circularity ' a sustainable model that aims to minimize waste and maximize material efficiency through recovery and reuse ' in the built environment: 600 million tons of construction and demolition waste was produced in the United States alone in 2018, with 820 million tons reported in the European Union, and an excess of 2 billion tons annually in China. This significant resource loss embedded in our current industrial ecosystem marks a linear economy that operates on a 'take-make-dispose' model of construction; in contrast, the 'make-use-reuse' approach of a circular economy offers an important opportunity to reduce environmental impacts. A team of MIT researchers has begun to assess what may be needed to spur widespread circular transition within the built environment in a new open-access study that aims to understand stakeholders' current perceptions of circularity and quantify their willingness to pay....

For many of the people served by the humanitarian sector, 2024 has been the worst of times. The most recent UN estimates of those forced to flee violence and disaster is a record of 120 million, a figure that has doubled in the past decade. The broader figure of those in humanitarian need, 300 million people, has been swelled by increasingly violent conflict and growing impacts of the climate crisis. Progress in meeting the UN's Sustainable Development Goals has also been either stagnating or declining in more than half of the fragile countries. A child born in those countries has a tenfold greater chance of being in poverty than one born in a stable state. The unprecedented numbers show the need for a new humanitarian surge: a technological one, harnessing the power of the digital and AI. For years we've (rightly) debated the risks and benefits of AI and waited for the promise of 'AI for Good' to arrive. In 2025, across the aid, development, and humanitarian sector, that moment may finally be at hand....

These explanations are often complex, however, perhaps containing information about hundreds of model features. And they are sometimes presented as multifaceted visualizations that can be difficult for users who lack machine-learning expertise to fully comprehend. They developed a two-part system that converts a machine-learning explanation into a paragraph of human-readable text and then automatically evaluates the quality of the narrative, so an end-user knows whether to trust it. 'Our goal with this research was to take the first step toward allowing users to have full-blown conversations with machine-learning models about the reasons they made certain predictions, so they can make better decisions about whether to listen to the model,' says Alexandra Zytek, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique. She is joined on the paper by Sara Pido, an MIT postdoc; Sarah Alnegheimish, an EECS graduate student; Laure Berti-Equille, a research director at the French National Research Institute for Sustainable Development; and senior author Kalyan Veeramachaneni, a principal research scientist in the Laboratory for Information and Decision Systems. The research will be presented at the IEEE Big Data Conference....

In a first for both universities, MIT undergraduates are engaged in research projects at the Universidad del Valle de Guatemala (UVG), while MIT scholars are collaborating with UVG undergraduates on in-depth field studies in Guatemala. These pilot projects are part of a larger enterprise, called ASPIRE (Achieving Sustainable Partnerships for Innovation, Research, and Entrepreneurship). Funded by the U.S. Agency for International Development, this five-year, $15-million initiative brings together MIT, UVG, and the Guatemalan Exporters Association to promote sustainable solutions to local development challenges. 'This research is yielding insights into our understanding of how to design with and for marginalized people, specifically Indigenous people,' says Elizabeth Hoffecker, co-principal investigator of ASPIRE at MIT and director of the MIT Local Innovation Group. The students' work is bearing fruit in the form of publications and new products ' directly advancing ASPIRE's goals to create an innovation ecosystem in Guatemala that can be replicated elsewhere in Central and Latin America....