{"id":15142437,"date":"2026-06-05T20:10:00","date_gmt":"2026-06-06T00:10:00","guid":{"rendered":"https:\/\/www.inthacity.com\/news\/the-crucial-human-component-in-computing-and-ai\/"},"modified":"2026-06-05T20:12:16","modified_gmt":"2026-06-06T00:12:16","slug":"the-crucial-human-component-in-computing-and-ai","status":"publish","type":"post","link":"https:\/\/www.inthacity.com\/news\/the-crucial-human-component-in-computing-and-ai\/","title":{"rendered":"The crucial human component in computing and AI"},"content":{"rendered":"

On April 30, the MIT Schwarzman College of Computing\u2019s Social and Ethical Responsibilities of Computing<\/a> (SERC) initiative hosted a full-day research symposium examining how artificial intelligence is shaping the world and its implications for society. <\/p>\n

The symposium included research talks by SERC\u2019s latest seed grant recipients on topics such as air pollution forecasting and responsible computer vision deployment, panels on AI alignment and AI in education, and a keynote address by Jon Kleinberg PhD \u201996, the Tisch University Professor of Computer Science and Information Science at Cornell University. The event also featured a poster session, where student researchers showcased projects <\/a>they worked on throughout the year as SERC Scholars<\/a>.<\/p>\n

\u201cThere is so much amazing research being done at MIT on how AI and computing can be forces for good that benefit humanity. It was inspiring to see so much community interest in all this cutting-edge work,\u201d said Brian Hedden, co-associate dean of SERC and professor of philosophy, who holds an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science (EECS).<\/p>\n

\u201cAs computing and AI become increasingly embedded in nearly every dimension of society, SERC\u2019s mission is to help ensure that ethical reflection and technical progress advance together,\u201d said Nikos Trichakis, co-associate dean of SERC and the J.C. Penney Professor of Management. \u201cThis year\u2019s symposium highlights the extraordinary range of work underway across MIT, and creates a forum for our community to engage deeply with the responsibilities that come with shaping the future of computing.\u201d<\/p>\n

Aligning AI with human values \u2014 and what values those might be<\/strong><\/p>\n

The challenges with AI alignment and moral meshing lie in the ethical questions of how to instill \u201chuman values\u201d onto a very powerful and rapidly changing technology. Who makes the decision on what values and rationalities are included in an ethical framework? How does one account for distortion when translating these values from user to machine? <\/p>\n

These questions, among others, were posed by Dylan Hadfield-Menell, associate professor of EECS, during a panel he moderated that brought together an interdisciplinary group of speakers.<\/p>\n

Iason Gabriel, a philosopher and research scientist at Google DeepMind, used the example of a judge to illustrate his point. \u201cYou want a judge to have good character, but to still interpret the rules. A reasonable person, though not necessarily the best person who ever lived. When it comes to AI, it\u2019s not appropriate to model it as perfect. AI should be doing what we tell it to do, while using its character to interpret according to our moral values.\u201d<\/p>\n

Bailey Flanigan, assistant professor of political science in a shared appointment with the MIT Schwarzman College of Computing in EECS, took this a step further. To her, the most important problem to AI alignment is \u201cresolving fundamental questions on who is entitled to govern different types of AI systems in the first place.\u201d<\/p>\n

Joining Flanigan on the panel was Bernado Zacka, associate professor of political science. Given the momentum of AI and complex institutional designs, Zacka expressed, \u201cone of the most urgent problems is understanding the wisdom contained in the systems we are replacing, and why they function the way they do.\u201d <\/p>\n

As deployment pressure increases, it can often feel like people are building the plane as they fly it, although the panelists overall seemed optimistic about the trajectory of AI alignment, emphasizing how crucial human components are to shaping these systems.<\/p>\n

Offloading versus uplifting<\/strong><\/p>\n

As students across all levels of education begin to use AI, questions arise on whether there\u2019s a way to ethically incorporate AI tools while maintaining academic accuracy and rigor. At a panel on AI and education, MIT faculty and Marta McAlister, the director of Gemini for Education, explored how AI is already being used in their classrooms and discussed ways it can support learning while remaining aligned with instructional and curricular goals.<\/p>\n

Professors Eric Klopfer and Samuel Madden, co-chairs of MIT\u2019s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, homed in on a central dilemma of whether AI is being used to offload work, rather than being used to help scaffold the concepts being taught. <\/p>\n

Madden, faculty head of computer science in EECS and the MIT College of Computing Distinguished Professor, described the process of cognitive struggle, whereby learning is done through a series of trials and failures. He said, \u201cstudents now, when they hit that wall, their first instinct is to ask AI. They don\u2019t see this as excelling in this process, and they haven\u2019t actually acquired the skill you\u2019re assessing.\u201d The question then becomes how instructors maintain the process of cognitive struggle so it provides just enough of a challenge to combat the urge to use AI. <\/p>\n

Klopfer, who serves as director of the Scheller Teacher Education Program and the Education Arcade at MIT, echoed similar sentiments, in that critical thinking is no longer becoming a crucial step in the output of the work. Regarding where to start in keeping material just challenging enough, Klopfer suggested examining the curriculum as a whole. \u201cSome core content has to go. We keep adding, instead of parsing or pruning,\u201d he said. <\/p>\n

Moderator Justin Reich, director of the Teaching Systems Lab and an associate professor in the Comparative Media Studies Program\/Writing, noted that while teens know that AI is bad, it doesn\u2019t necessarily stop their AI usage. However, by inviting them into the discussion on how AI is implemented and incorporating a more reflective exchange with instructors, students could be more equipped to choose how they use these tools and why.<\/p>\n

Regardless, AI tools and their implementation should not be treated as a one-size-fits-all policy. Pat Pataranutaporn, the Asahi Broadcasting Corporation Career Development Professor of Media Arts and Sciences and head of the Cyborg Psychology research group at the MIT Media Lab, said, \u201cAI is not just one thing. It can and should be designed differently to promote things like creativity and critical thinking. What we measure, and how, shouldn\u2019t be about getting the answer right. We should think about it would really mean for a student to learn these days.\u201d<\/p>\n

Is mimicking human reasoning just as good as the real thing?<\/strong><\/p>\n

With a slide deck that included chess grandmasters and film references, Kleinberg\u2019s keynote address, titled \u201cAI\u2019s Models of the World, and Ours,\u201d evaluated instances where AI systems have inadvertently set us up to fail due to a mismatch between the system\u2019s model of the world and ours. <\/p>\n

To illustrate this point, Kleinberg used chess, where modern chess engines can compete at superhuman levels, but when paired with human partners, their strategies aren\u2019t understandable or inferable to their human counterpart. These human handoffs would then lead to confusion. Kleinberg used the example of \u201cThe Fellowship of the Ring,\u201d where Gandalf, a powerful wizard, entrusts a highly dangerous and important quest to a ragtag group of adventurers. For those familiar with the story, the group is unexpectedly left without Gandalf\u2019s guidance, sending them into a temporary bout of very serious turmoil. <\/p>\n

When the chess engine hands a turn over to its human partner, the human struggles to pick up on the predictive move pattern that the engine has been following up until this point. \u201cThe danger of human-algorithm teams is that when the human takes over, the algorithm knows what it wants to do next, but the human doesn\u2019t,\u201d explained Kleinberg.<\/p>\n

These analogies showcase the differences in the ways AI understands a world \u2014 through predictive simulations, pattern recognition, and constraints \u2014 to mimic human reasoning versus the innate, embodied knowledge that comes with the human experience, and whether these systems truly understand the worlds in which they\u2019re operating. But the question remains that if the game still results in a checkmate, does it matter?<\/p>\n

Source: News Boston \u2013 MIT<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The MIT Ethics of Computing Research Symposium brought together experts and researchers working at the heart of ethical and social impact in technology.<\/p>\n","protected":false},"author":1,"featured_media":15142439,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[166],"tags":[],"class_list":["post-15142437","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-boston"],"featured_image_urls":{"full":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing.webp",1500,1000,false],"thumbnail":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-300x200.jpg",300,200,true],"medium":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-620x413.jpg",620,413,true],"medium_large":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-768x512.jpg",768,512,true],"large":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-940x627.jpg",940,627,true],"1536x1536":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing.webp",1500,1000,false],"2048x2048":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing.webp",1500,1000,false],"post-thumbnail":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-998x665.jpg",998,665,true],"ignition_item":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-670x446.jpg",670,446,true],"ignition_item_lg":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-1340x894.jpg",1340,894,true],"ignition_article_media":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-510x510.jpg",510,510,true],"ignition_minicart_item":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-160x160.jpg",160,160,true],"profile_24":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-24x24.jpg",24,24,true],"profile_48":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-48x48.jpg",48,48,true],"profile_96":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-96x96.jpg",96,96,true],"profile_150":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-150x150.jpg",150,150,true],"profile_300":["https:\/\/www.inthacity.com\/news\/wp-content\/uploads\/2026\/06\/15142437-mit-schwarzman-ethics-of-computing-300x300.jpg",300,300,true]},"author_info":{"display_name":"news.iNthacity","author_link":"https:\/\/www.inthacity.com\/news\/author\/atombo\/"},"category_info":"Boston<\/a>","tag_info":"Boston","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/posts\/15142437","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/comments?post=15142437"}],"version-history":[{"count":1,"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/posts\/15142437\/revisions"}],"predecessor-version":[{"id":15142438,"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/posts\/15142437\/revisions\/15142438"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/media\/15142439"}],"wp:attachment":[{"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/media?parent=15142437"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/categories?post=15142437"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inthacity.com\/news\/wp-json\/wp\/v2\/tags?post=15142437"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}