# Technology and society Published articles for Technology and society. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Naoki Egami's Research on Political Methodology and External Validity DevFeed: [Naoki Egami's Research on Political Methodology and External Validity]() Original publisher: [Read original article]() Author: Peter Dizikes | MIT News Published: 2026-09-16T04:00:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [AI Research]() Tags: [artificial-intelligence](), [external-validity](), [faculty](), [idss](), [mit-political-science](), [mit-schwarzman-college-of-computing](), [naoki-egami](), [political-methodology](), [political-science](), [profile](), [research](), [school-of-humanities-arts-and-social-sciences](), [science](), [social-sciences](), [statistics](), [technology-and-society](), [voting-and-elections]() ### AI overview An MIT profile of political scientist Naoki Egami, whose research examines research methodology, external validity, and the mathematical and statistical challenges of studying civic and political phenomena. It also discusses his work on the use of AI tools in research. ### Source excerpt Naoki Egami has become a standout in political methodology, helping refine tools that give scholars durable results. ## Ila Kumar: Innovating with communities DevFeed: [Ila Kumar: Innovating with communities]() Original publisher: [Read original article]() Author: Gitana Savage | MIT News correspondent Published: 2026-09-01T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [digital](), [context]() Tags: [artificial-intelligence](), [assistive-technology](), [child-welfare](), [communities](), [digital](), [engineering-systems](), [graduate-postdoctoral](), [ila-kumar](), [lifelong-kindergarten](), [media-lab](), [mental-health](), [mit-graduate-students](), [profile](), [psychology](), [school-of-architecture-and-planning](), [students](), [tech-for-good](), [technology](), [technology-and-society]() ### AI overview MIT PhD student Ila Kumar researches community-based design, involving young people who have experienced childhood trauma in shaping digital technologies intended to support healing, connection, independence, and well-being. ### Source excerpt The PhD student works to give young people an active role in shaping digital technologies that can support their own well-being. ## When AI art has no author: Study finds generated images often can't be traced to training data DevFeed: [When AI art has no author: Study finds generated images often can't be traced to training data]() Original publisher: [Read original article]() Author: Rachel Gordon | MIT CSAIL Published: 2026-08-18T16:35:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [datasets](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)]() Tags: [ablation](), [ai](), [ai-and-copyright-law](), [ai-generated-images](), [ai-governance](), [artificial-intelligence](), [arts](), [arts-technology-and-society](), [attribution-decay](), [causal-inference](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [counterfactual-analysis](), [counterfactual-radius](), [data](), [data-attribution](), [datasets](), [david-gifford](), [diffusion-ensembles](), [electrical-engineering-and-computer-science-eecs](), [ethics](), [generative-ai-images](), [generative-diffusion-models](), [image-similarity-metrics](), [images](), [law](), [machine-learning](), [machine-unlearning](), [mit-csail](), [mit-schwarzman-college-of-computing](), [model](), [model-interpretability](), [paper](), [privacy-preserving-machine-learning](), [research](), [school-of-engineering](), [science](), [technology-and-policy](), [technology-and-society](), [training-data-attribution](), [training-data-influence](), [zheng-dai]() ### AI overview MIT CSAIL researchers describe attribution decay, a phenomenon in which the influence of individual training examples on a generative model's outputs diminishes as datasets grow. Their method removes training examples and retrains models to test whether generated samples change. ### Source excerpt A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves. ## The benefits of medical AI assistance vary based on user expertise DevFeed: [The benefits of medical AI assistance vary based on user expertise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-08-04T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Human-AI evaluation](), [AI Development](), [AI Chat]() Tags: [ai](), [artificial-intelligence](), [bias](), [computer-science-and-technology](), [decision-making](), [dermatological-diagnosis](), [diagnosing-skin-disease](), [diagnostics](), [electrical-engineering-and-computer-science-eecs](), [explainability](), [explainable-ai](), [health-care](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [jameel-clinic](), [laboratory-for-information-and-decision-systems-lids](), [large-language-model](), [machine-learning](), [marzyeh-ghassemi](), [medicine](), [research](), [technology-and-society](), [users]() ### AI overview A study found that AI assistance improved skin-disease diagnosis for non-experts and clinicians, but explainability affected users differently. Non-experts often deferred to LLM-based explanations even when the AI was wrong, while clinicians performed best with the model's prediction alone. ### Source excerpt Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors. ## Bailey Flanigan uses computational and mathematical tools to study democratic participation DevFeed: [Bailey Flanigan uses computational and mathematical tools to study democratic participation]() Original publisher: [Read original article]() Author: Michaela Jarvis | MIT Laboratory for Information and Decision Systems Published: 2026-07-17T17:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Computer science](), [Electrical engineering and computer science (EECS)](), [math](), [MIT Schwarzman College of Computing]() Tags: [ai-and-democracy](), [ai-in-politics](), [algorithms](), [artificial-intelligence](), [bailey-flanigan](), [computer-science](), [computer-science-and-technology](), [defending-democracy](), [democracy](), [democratic-decision-making](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [government](), [labor-and-jobs](), [laboratory-for-information-and-decision-systems-lids](), [math](), [mit-eecs-faculty](), [mit-faculty-profile](), [mit-lids](), [mit-political-science](), [mit-schwarzman-college-of-computing](), [political-legitimacy](), [political-science](), [politics](), [profile](), [public-health](), [research](), [school-of-engineering](), [school-of-humanities-arts-and-social-sciences](), [science](), [technology-and-society](), [wisconsin-innocence-project]() ### AI overview An MIT faculty member, Bailey Flanigan studies how computational and mathematical tools can create new avenues for meaningful democratic participation. The article describes her interdisciplinary path across computer science, political science, medicine, public health, economics, and related fields. ### Source excerpt Assistant Professor Bailey Flanigan has arrived at complex computational methods for helping democracy thrive. ## 3 Questions: Neural transparency and the future of AI design DevFeed: [3 Questions: Neural transparency and the future of AI design]() Original publisher: [Read original article]() Author: Media Lab Published: 2026-07-15T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Neural Network](), [User Interfaces](), [interface](), [Language models]() Tags: [3-questions](), [ai](), [ai-companion](), [ai-empathy](), [ai-hallucinations](), [ai-personalization](), [ai-safety](), [ai-sycophancy](), [ai-toxicity](), [algorithms](), [anthony-baez](), [apps](), [artificial-intelligence](), [behavioral-prediction](), [character-ai](), [chatbot-behavior](), [computer-science-and-technology](), [data](), [ethics](), [faculty](), [human-ai-interaction](), [human-computer-interaction](), [interface](), [interview](), [language-models](), [large-language-models](), [machine-learning](), [mechanistic-interpretability](), [media-lab](), [mental-health](), [mit-faculty-interview](), [mit-media-lab](), [neural](), [neural-transparency](), [pat-pataranutaporn](), [persona-scores](), [persona-vectors](), [personalized-ai](), [research](), [safety](), [school-of-architecture-and-planning](), [sheer-karny](), [sunburst-visualization](), [technology-and-society](), [transparency](), [users]() ### AI overview An MIT Media Lab team introduces "neural transparency," an interface that visualizes internal patterns in AI models to help users anticipate how personalized chatbots may behave. The approach compares model activations associated with contrasting traits such as empathy, honesty, toxicity, hallucination, and sycophancy, then maps custom system prompts onto those behavior directions. ### Source excerpt Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word. ## How MIT students are helping to prevent cyberattacks DevFeed: [How MIT students are helping to prevent cyberattacks]() Original publisher: [Read original article]() Author: Nicole Estvanik Taylor | Department of Urban Studies and Planning Published: 2026-07-13T19:10:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Cybersecurity](), [Security & Privacy](), [ransomware](), [Business Security]() Tags: [ai-and-security](), [ai-in-cyber-crime](), [artificial-intelligence](), [attacks](), [business-and-management](), [classes-and-programs](), [collaboration](), [computer-science-and-technology](), [cyber-attacks](), [cybersecurity](), [government](), [health-care](), [jungwoo-chun](), [lawrence-susskind](), [local](), [massive-open-online-courses-moocs](), [mit-class-11-074-11-274-cybersecurity-clinic](), [mit-cybersecurity-clinic](), [mit-dusp](), [mitx](), [organizations](), [prevent](), [public-sector](), [ransomware-attacks](), [school-of-architecture-and-planning](), [software](), [technology-and-society](), [training](), [urban-studies-and-planning](), [vulnerabilities]() ### AI overview MIT's Cybersecurity Clinic trains students to assess cyberattack vulnerabilities and provide free, confidential security assessments to municipalities, health-care organizations, and other at-risk communities. ### Source excerpt Students from the MIT Cybersecurity Clinic help local governments and other vulnerable organizations defend against digital threats. ## MIT in the media: For the future of tech, "Massachusetts can absolutely lead" DevFeed: [MIT in the media: For the future of tech, "Massachusetts can absolutely lead"]() Original publisher: [Read original article]() Published: 2026-06-18T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Strategy](), [Robotics]() Tags: [ai](), [alumni-ae](), [applied-ai](), [articles](), [artificial-intelligence](), [biotechnology](), [cambridge-boston-and-region](), [computer-science-and-technology](), [courses](), [energy](), [faculty](), [funding](), [health](), [manufacturing](), [president-sally-kornbluth](), [quantum-computing](), [quantum-technologies](), [research](), [robotics](), [startup](), [startups](), [students](), [tech](), [technology-and-society]() ### AI overview MIT's research, AI initiatives, online courses, and entrepreneurship programs are highlighted in coverage of Massachusetts' technology ecosystem and its potential for continued leadership. ### Source excerpt Leaders, faculty across MIT discuss fostering innovation and talent in Greater Boston in special series of articles published alongside the outlet's annual list of 'Tech Power Players' ## Startup's nuclear-inspired cooling system could make data centers more sustainable DevFeed: [Startup's nuclear-inspired cooling system could make data centers more sustainable]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-06-10T04:00:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [data centers](), [Artificial Intelligence](), [cloud-infrastructure](), [Computer science]() Tags: [ai](), [ai-data-centers](), [artificial-intelligence](), [computer-science-and-technology](), [cooling](), [data-center-cooling](), [data-center-sustainability](), [data-centers](), [efficiency](), [energy](), [energy-efficiency](), [energy-storage](), [ferveret](), [matteo-bucci](), [nuclear-science-and-engineering](), [power](), [reza-azizian](), [school-of-engineering](), [startups](), [sustainability](), [sustainable-computing](), [technology-and-society](), [water]() ### AI overview Ferveret, a startup founded by two MIT researchers, is adapting nuclear-reactor heat-transfer methods to cool AI data-center servers with a specialized liquid. Its Adaptive Phase Cooling system uses small, frequently detaching bubbles to improve heat transfer without water and with less electricity. ### Source excerpt Founded by two researchers from MIT, Ferveret reduces the amount of energy and water required to cool the chips that power AI.