--- type: "firecrawl-provider" description: "MIT (mit.edu) public journalism: MIT News (news.mit.edu) stories from its official RSS feeds by topic, school or section, and any story as Markdown with the attribution MIT News asks republishers to show; and MIT Sloan 'Ideas Made to Matter' (mitsloan.mit.edu) articles by topic, and any article as Markdown with key points and writers. MIT OpenCourseWare is not covered." use_when: "MIT (mit.edu) public journalism: MIT News (news.mit.edu) stories from its official RSS feeds by topic, school or section, and any story as Markdown with the attribution MIT News asks republishers to show; and MIT Sloan 'Ideas Made to Matter' (mitsloan.mit.edu) articles by topic, and any article as Markdown with key points and writers. MIT OpenCourseWare is not covered." categories: "News" capabilities: 4 credits_per_call: 5 --- # MIT News and MIT Sloan on Firecrawl Alexandria MIT (mit.edu) public journalism: MIT News (news.mit.edu) stories from its official RSS feeds by topic, school or section, and any story as Markdown with the attribution MIT News asks republishers to show; and MIT Sloan 'Ideas Made to Matter' (mitsloan.mit.edu) articles by topic, and any article as Markdown with key points and writers. MIT OpenCourseWare is not covered. - Categories: News - Category index: [News category](https://firecrawl.dev/alexandria/agents/categories/news) - Provider key: `mit-edu` - Access: Firecrawl credits - Cost: 5 credits per call - Delivery: First 100 words of each matching article ## More - [Human guide](https://firecrawl.dev/app/alexandria/mit-edu) - [OpenAPI spec](https://firecrawl.dev/alexandria/agents/providers/mit-edu/openapi.json) ## Capabilities - [Get news article](https://firecrawl.dev/alexandria/agents/providers/mit-edu/news/get_news_article): One MIT News article as clean Markdown with headline, dek (subtitle), writers and their office, exact publication time and related topics, plus the attribution MIT News requires when its stories are republished (headline, byline, link to the original and the notice 'Reprinted with permission of MIT News'). Photos are not included (MIT licenses them separately). - [Get sloan article](https://firecrawl.dev/alexandria/agents/providers/mit-edu/news/get_sloan_article): One MIT Sloan 'Ideas Made to Matter' article as clean Markdown with title, summary, the 'What you need to know' key points, writers (with profile links), publication and update times, primary topic and read time. Newsletter forms, course promos and related-article rails are removed. - [List news](https://firecrawl.dev/alexandria/agents/providers/mit-edu/news/list_news): Latest MIT News (news.mit.edu) stories from the site's official RSS feeds: the all-news feed, the campus or research section, one of the five schools, or any MIT News topic (artificial-intelligence2, climate-change-and-sustainability, physics, ...). Each item has title, URL, publication time, writer and office, and the story dek. Feeds hold the 50 newest stories; for older stories pass page to read the topic archive 15 at a time. q keeps only items whose title, dek or writer contain every word. Accepts a pasted topic page or feed URL. - [List sloan articles](https://firecrawl.dev/alexandria/agents/providers/mit-edu/news/list_sloan_articles): The latest MIT Sloan 'Ideas Made to Matter' articles (mitsloan.mit.edu), from the hub or one topic page (artificial-intelligence, leadership, finance, work-careers, ...): title, URL, slug, primary topic, writers, summary and date for the ten newest, as the site lists them. ## 1. Choose this provider when MIT (mit.edu) public journalism: MIT News (news.mit.edu) stories from its official RSS feeds by topic, school or section, and any story as Markdown with the attribution MIT News asks republishers to show; and MIT Sloan 'Ideas Made to Matter' (mitsloan.mit.edu) articles by topic, and any article as Markdown with key points and writers. MIT OpenCourseWare is not covered. ## Article preview policy News providers return metadata plus the first 100 words of each matching article. They are not wrapped third-party APIs and do not return full article text. ## 2. Minimal request Call `POST https://api.firecrawl.dev/v2/scrape` with `{ alexandria: { provider, capability, options } }`. For a batch, send `{ alexandria: [...] }` with up to 10 calls. ```json { "provider": "mit-edu", "capability": "news/get_news_article", "options": { "url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019" } } ``` ## 3. Add provider options Use only the options needed for the task: - `url` (string, required): Article URL such as https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019, or its path 2026/. Example: `` ## 4. Request through your preferred interface ### JavaScript ```javascript const result = await firecrawl.scrape({ alexandria: { provider: "mit-edu", capability: "news/get_news_article", options: { url: "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019", }, }, }); ``` ### Python ```python result = firecrawl.scrape_alexandria({ "provider": "mit-edu", "capability": "news/get_news_article", "options": { "url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019" } }) ``` ### cURL ```sh curl https://api.firecrawl.dev/v2/scrape \ -H "Authorization: Bearer $FIRECRAWL_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "alexandria": { "provider": "mit-edu", "capability": "news/get_news_article", "options": { "url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019" } } }' ``` ### CLI ```sh firecrawl scrape 'mit-edu/news/get_news_article' \ --options '{"url":"https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019"}' ``` ### MCP Call the FCX MCP retrieve tool with this object: ```json { "provider": "mit-edu", "capability": "news/get_news_article", "options": { "url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019" } } ``` Ask for only the returned fields needed by the task. ## 5. Full request shape ```json { "provider": "mit-edu", "capability": "news/get_news_article", "options": { "url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019" } } ``` ## 6. Response data The response includes `success`, `provider`, `capability`, `creditsCost` and `data`. This example shows the provider payload in `data`: ```json { "attribution": { "homepage": "https://news.mit.edu/", "notice": "Reprinted with permission of MIT News", "original_url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019", "publisher": "MIT News" }, "author_affiliation": "Abdul Latif Jameel Clinic for Machine Learning in Health", "authors": [ "Alex Ouyang" ], "content": "Despite millions of years of evolution, the development of the human fetus remains a complex process that is vulnerable to genetic, environmental, and metabolic disruptions. These disruptions could result in developmental abnormalities, starting with one of the first organs to begin developing in the fetus that continues to develop after birth: the brain.\n\nIn a routine prenatal ultrasound, the sonographer will flag possible brain abnormalities for additional magnetic resonance imaging (MRI). But imperfect imaging can result in missed or incorrect diagnoses.\n\nTo assist with the early detection and treatment of these abnormalities, researchers at MIT, Boston Children’s Hospital, and Inria have developed and trained a deep learning model called E(3)-Pose that helps image the fetus’s head while it’s in motion to capture a high-resolution image that the radiologist can examine. A paper about the work was presented at the [2026 European Conference on Computer Vision](https://eccv.ecva.net/).\n\n“One of the problems with having the baby move is that the slices are all over the place,” says first author Ramya Muthukrishnan. Muthukrishnan is an MIT PhD student in the lab of Sunlin (1966) and Priscilla Chou Professor of Electrical Engineering and Computer Science Polina Golland, who is a principal investigator at the [MIT Abdul Latif Jameel Clinic for Machine Learning in Health](https://jclinic.mit.edu/) (Jameel Clinic) and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).\n\nBy slices, Muthukrishnan is referring to a series of 2D images taken during a fetal MRI scan. These 2D planes stacked together effectively make up one high-resolution, 3D image of the fetus’s head. But if the head shifts during imaging, each 2D image “slice” could end up being taken from a different angle, resulting in gaps in the final scan and unusual slice orientation. These problems may cause the physician reviewing the MRI to miss or incorrectly diagnose a pathology in the fetal brain.\n\n“There are very fine details that you need a very good volumetric picture of the brain to see,” explains Muthukrishnan. “If you have coverage gaps, you can’t see critical regions, and you need to be able to see well to detect abnormal development.”\n\nWhat makes E(3)-Pose particularly good at tracking fetal motion is its focus on physics. Rather than simply training the model to learn physics from the data, the very principles of physics are baked into the neural network: left-right symmetry of the fetal head is embedded in the model architecture, making it easier for the model to accurately identify the brain’s orientation.\n\nWhen Muthukrishnan began working on the project at the start of her PhD, she had no background in physics, let alone MRI physics. Most of her experience was in computer science. “I was surprised when I had to go and implement this on the scanner,” she recalls. “I was not prepared for how many times I would crash the scanner because putting things on hardware and getting it to work on hardware is a different beast than training a model to run on data on a GPU.”\n\nDespite the challenges, Muthukrishnan persisted. She remembers shadowing a radiologist who performed fetal MRI scans at Boston Children’s Hospital at the start of her PhD and observing the shared frustration of the patient and radiologist: the pregnant mother felt uncomfortable after spending an hour in an MRI, only to quit the scan halfway; the radiologist was unable to procure a full, high-quality MRI to monitor the development of the fetus.\n\n“Seeing frustration about it helped me decide what I wanted to do with my PhD, because it felt like this was a pain point,” Muthukrishnan says.\n\nFortunately, she had help: Co-author Borjan Gagoski, an assistant professor of radiology at Harvard Medical School and a researcher at Boston Children's Hospital, developed a sequence that acquires additional MRI scans to assist with training E(3)-Pose and worked with Muthukrishnan to help troubleshoot the scanner whenever it crashed. Co-author and Boston Children’s Research Assistant Aryn Lee recruited the pregnant volunteers and assisted with acquiring each of the MRI scans. Senior authors who helped guide the project include Elfar Adalsteinsson, MIT professor of electrical engineering and computer science; Ellen Grant, the Käthe Beutler Professor of Pediatrics and professor of radiology at Harvard Medical School; and Golland. Benjamin Billot, a researcher at Inria and a former postdoc in Golland’s group, helped Muthukrishnan get started with the initial method he developed.\n\n“Ramya’s work elegantly combines geometric concepts like equivariance and symmetry with the power of deep learning to change how MRI scanners acquire the images, and to ultimately help the physicians detect and treat problems early in development. It’s an impressive research project that is deep and fundamental in how it models image acquisition, with clear clinical impact,” says Golland.\n\nGrant, who directs the Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC) at Boston Children’s, adds that Muthukrishnan’s work “will automate fetal brain MRI, replacing today's slow, manual process with faster and more comfortable scans for mothers, while also providing higher-quality images for radiologists to interpret.”\n\nE(3)-Pose’s applications could extend beyond motion tracking for fetal MRIs. It could assist with imaging other hard-to-image anatomy, and even autonomous driving applications, where limited sensors need to capture noisy, 3D data in order to track 3D objects.\n\nFor now, Muthukrishnan hopes to improve E(3)-Pose so that it can help with imaging second-trimester or younger fetuses, as the model is primarily trained on third trimester fetuses. Second-trimester fetuses are “so small, they move around a lot more because there’s more space in the uterus,” Muthukrishnan says. “They’re more important to focus on because the earlier you detect brain abnormalities, the easier it is to intervene.”\n\nThis work is supported by the MIT Jameel Clinic, the MIT Takeda Program, the Chou Family Transformative Research Fund, the National Institute of Biomedical Imaging and Bioengineering, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development.", "observed_at_ms": 1792693914652, "published_at": "2026-10-19T18:20:00Z", "subtitle": "A model developed by researchers at MIT and Boston Children's Hospital adjusts scans on the fly to track fetal movement, potentially sparing families repeat hospital visits.", "title": "Why teaching AI about symmetry could make prenatal diagnosis more reliable", "topics": [ { "name": "Research", "slug": "research" }, { "name": "Imaging", "slug": "imaging" }, { "name": "Health care", "slug": "health-care" }, { "name": "Medical devices", "slug": "medical-devices" }, { "name": "Artificial intelligence", "slug": "artificial-intelligence2" }, { "name": "Computer vision", "slug": "computer-vision" }, { "name": "Machine learning", "slug": "machine-learning" }, { "name": "Health sciences and technology", "slug": "health" }, { "name": "Electrical engineering and computer science (EECS)", "slug": "electrical-engineering-computer-science-eecs" }, { "name": "Computer science and technology", "slug": "computers" }, { "name": "MIT-IBM Computing Research Lab", "slug": "mit-ibm-computing-research-lab" }, { "name": "Jameel Clinic", "slug": "jameel-clinic" }, { "name": "Computer Science and Artificial Intelligence Laboratory (CSAIL)", "slug": "computer-science-and-artificial-intelligence-laboratory-csail" }, { "name": "School of Engineering", "slug": "school-engineering" }, { "name": "MIT Schwarzman College of Computing", "slug": "mit-schwarzman-college-computing" }, { "name": "National Institutes of Health (NIH)", "slug": "nih" } ], "url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019", "word_count": 970 } ``` ## API reference-derived contract This provider returns article metadata and a 100-word preview; use the source URL for the complete article. ### Get news article - Capability: `news/get_news_article` - Description: One MIT News article as clean Markdown with headline, dek (subtitle), writers and their office, exact publication time and related topics, plus the attribution MIT News requires when its stories are republished (headline, byline, link to the original and the notice 'Reprinted with permission of MIT News'). Photos are not included (MIT licenses them separately). - Instructions: To read the full text of a news.mit.edu story, for example one listed by list_news or a pasted news.mit.edu/YYYY/... link. - Cost: 5 credits per call - Capability file: [Get news article](https://firecrawl.dev/alexandria/agents/providers/mit-edu/news/get_news_article) Accepted options: - `url` (string, required): Article URL such as https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019, or its path 2026/. Example: `` Response schema example: ```json { "attribution": { "homepage": "https://news.mit.edu/", "notice": "Reprinted with permission of MIT News", "original_url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019", "publisher": "MIT News" }, "author_affiliation": "Abdul Latif Jameel Clinic for Machine Learning in Health", "authors": [ "Alex Ouyang" ], "content": "Despite millions of years of evolution, the development of the human fetus remains a complex process that is vulnerable to genetic, environmental, and metabolic disruptions. These disruptions could result in developmental abnormalities, starting with one of the first organs to begin developing in the fetus that continues to develop after birth: the brain.\n\nIn a routine prenatal ultrasound, the sonographer will flag possible brain abnormalities for additional magnetic resonance imaging (MRI). But imperfect imaging can result in missed or incorrect diagnoses.\n\nTo assist with the early detection and treatment of these abnormalities, researchers at MIT, Boston Children’s Hospital, and Inria have developed and trained a deep learning model called E(3)-Pose that helps image the fetus’s head while it’s in motion to capture a high-resolution image that the radiologist can examine. A paper about the work was presented at the [2026 European Conference on Computer Vision](https://eccv.ecva.net/).\n\n“One of the problems with having the baby move is that the slices are all over the place,” says first author Ramya Muthukrishnan. Muthukrishnan is an MIT PhD student in the lab of Sunlin (1966) and Priscilla Chou Professor of Electrical Engineering and Computer Science Polina Golland, who is a principal investigator at the [MIT Abdul Latif Jameel Clinic for Machine Learning in Health](https://jclinic.mit.edu/) (Jameel Clinic) and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).\n\nBy slices, Muthukrishnan is referring to a series of 2D images taken during a fetal MRI scan. These 2D planes stacked together effectively make up one high-resolution, 3D image of the fetus’s head. But if the head shifts during imaging, each 2D image “slice” could end up being taken from a different angle, resulting in gaps in the final scan and unusual slice orientation. These problems may cause the physician reviewing the MRI to miss or incorrectly diagnose a pathology in the fetal brain.\n\n“There are very fine details that you need a very good volumetric picture of the brain to see,” explains Muthukrishnan. “If you have coverage gaps, you can’t see critical regions, and you need to be able to see well to detect abnormal development.”\n\nWhat makes E(3)-Pose particularly good at tracking fetal motion is its focus on physics. Rather than simply training the model to learn physics from the data, the very principles of physics are baked into the neural network: left-right symmetry of the fetal head is embedded in the model architecture, making it easier for the model to accurately identify the brain’s orientation.\n\nWhen Muthukrishnan began working on the project at the start of her PhD, she had no background in physics, let alone MRI physics. Most of her experience was in computer science. “I was surprised when I had to go and implement this on the scanner,” she recalls. “I was not prepared for how many times I would crash the scanner because putting things on hardware and getting it to work on hardware is a different beast than training a model to run on data on a GPU.”\n\nDespite the challenges, Muthukrishnan persisted. She remembers shadowing a radiologist who performed fetal MRI scans at Boston Children’s Hospital at the start of her PhD and observing the shared frustration of the patient and radiologist: the pregnant mother felt uncomfortable after spending an hour in an MRI, only to quit the scan halfway; the radiologist was unable to procure a full, high-quality MRI to monitor the development of the fetus.\n\n“Seeing frustration about it helped me decide what I wanted to do with my PhD, because it felt like this was a pain point,” Muthukrishnan says.\n\nFortunately, she had help: Co-author Borjan Gagoski, an assistant professor of radiology at Harvard Medical School and a researcher at Boston Children's Hospital, developed a sequence that acquires additional MRI scans to assist with training E(3)-Pose and worked with Muthukrishnan to help troubleshoot the scanner whenever it crashed. Co-author and Boston Children’s Research Assistant Aryn Lee recruited the pregnant volunteers and assisted with acquiring each of the MRI scans. Senior authors who helped guide the project include Elfar Adalsteinsson, MIT professor of electrical engineering and computer science; Ellen Grant, the Käthe Beutler Professor of Pediatrics and professor of radiology at Harvard Medical School; and Golland. Benjamin Billot, a researcher at Inria and a former postdoc in Golland’s group, helped Muthukrishnan get started with the initial method he developed.\n\n“Ramya’s work elegantly combines geometric concepts like equivariance and symmetry with the power of deep learning to change how MRI scanners acquire the images, and to ultimately help the physicians detect and treat problems early in development. It’s an impressive research project that is deep and fundamental in how it models image acquisition, with clear clinical impact,” says Golland.\n\nGrant, who directs the Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC) at Boston Children’s, adds that Muthukrishnan’s work “will automate fetal brain MRI, replacing today's slow, manual process with faster and more comfortable scans for mothers, while also providing higher-quality images for radiologists to interpret.”\n\nE(3)-Pose’s applications could extend beyond motion tracking for fetal MRIs. It could assist with imaging other hard-to-image anatomy, and even autonomous driving applications, where limited sensors need to capture noisy, 3D data in order to track 3D objects.\n\nFor now, Muthukrishnan hopes to improve E(3)-Pose so that it can help with imaging second-trimester or younger fetuses, as the model is primarily trained on third trimester fetuses. Second-trimester fetuses are “so small, they move around a lot more because there’s more space in the uterus,” Muthukrishnan says. “They’re more important to focus on because the earlier you detect brain abnormalities, the easier it is to intervene.”\n\nThis work is supported by the MIT Jameel Clinic, the MIT Takeda Program, the Chou Family Transformative Research Fund, the National Institute of Biomedical Imaging and Bioengineering, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development.", "observed_at_ms": 1792693914652, "published_at": "2026-10-19T18:20:00Z", "subtitle": "A model developed by researchers at MIT and Boston Children's Hospital adjusts scans on the fly to track fetal movement, potentially sparing families repeat hospital visits.", "title": "Why teaching AI about symmetry could make prenatal diagnosis more reliable", "topics": [ { "name": "Research", "slug": "research" }, { "name": "Imaging", "slug": "imaging" }, { "name": "Health care", "slug": "health-care" }, { "name": "Medical devices", "slug": "medical-devices" }, { "name": "Artificial intelligence", "slug": "artificial-intelligence2" }, { "name": "Computer vision", "slug": "computer-vision" }, { "name": "Machine learning", "slug": "machine-learning" }, { "name": "Health sciences and technology", "slug": "health" }, { "name": "Electrical engineering and computer science (EECS)", "slug": "electrical-engineering-computer-science-eecs" }, { "name": "Computer science and technology", "slug": "computers" }, { "name": "MIT-IBM Computing Research Lab", "slug": "mit-ibm-computing-research-lab" }, { "name": "Jameel Clinic", "slug": "jameel-clinic" }, { "name": "Computer Science and Artificial Intelligence Laboratory (CSAIL)", "slug": "computer-science-and-artificial-intelligence-laboratory-csail" }, { "name": "School of Engineering", "slug": "school-engineering" }, { "name": "MIT Schwarzman College of Computing", "slug": "mit-schwarzman-college-computing" }, { "name": "National Institutes of Health (NIH)", "slug": "nih" } ], "url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019", "word_count": 970 } ``` ### Get sloan article - Capability: `news/get_sloan_article` - Description: One MIT Sloan 'Ideas Made to Matter' article as clean Markdown with title, summary, the 'What you need to know' key points, writers (with profile links), publication and update times, primary topic and read time. Newsletter forms, course promos and related-article rails are removed. - Instructions: To read the full text of a mitsloan.mit.edu/ideas-made-to-matter/... article, e.g. one listed by list_sloan_articles. - Cost: 5 credits per call - Capability file: [Get sloan article](https://firecrawl.dev/alexandria/agents/providers/mit-edu/news/get_sloan_article) Accepted options: - `slug` (string): Article slug as listed by list_sloan_articles. Pattern: ^[A-Za-z0-9_-]+$. Example: `` - `url` (string): Article URL, e.g. https://mitsloan.mit.edu/ideas-made-to-matter/a-framework-determining-when-ai-can-make-decisions. Pattern: ^https?://. Example: `` Response schema example: ```json { "authors": [ { "name": "Beth Stackpole", "url": "https://mitsloan.mit.edu/external-contacts/beth-stackpole" } ], "content": "What tasks should artificial intelligence be trusted with, and when should humans remain involved? Designing those decision rights is one of the defining challenges companies face in the AI era.\n\nResearchers from the [MIT Center for Information Systems Research](https://cisr.mit.edu/) have [created a framework that can help](https://cisr.mit.edu/publication/2026_0601_AIDecisionMatrix_SebastianWeillHaskampVomBrocke). Using the AI Decision Matrix, business leaders can determine how humans and autonomous AI agents should participate in decision-making based on two dimensions: ambiguity and risk.\n\n[Ina Sebastian](https://mitsloan.mit.edu/faculty/directory/ina-maria-sebastian), [Peter Weill](https://mitsloan.mit.edu/faculty/directory/peter-weill), [Thomas Haskamp](https://www.wi.uni-muenster.de/department/bpm/people/thomas-haskamp), and [Jan vom Brocke](https://janvombrocke.com/) developed the matrix after conducting interviews with 30 executives. It’s based on the premise that not all decisions are equal: Some are predictable and low risk, while others are ambiguous and consequential.\n\n“This requires different approaches to automation and oversight,” the researchers write in a recent research briefing. “The same AI capability can be safe in one context and risky in another.”\n\n## Dimensions and steps involved in decision-making\n\nThe AI Decision Matrix considers two dimensions of decision-making:\n\n- **Ambiguity**, which describes how clearly data determines the answer and to what extent people agree on it. Low-ambiguity decisions are repeatable and predictable, while high-ambiguity decisions allow for multiple interpretations.\n- **Risk** refers to the consequences of getting a decision wrong. Low-risk decisions have little impact if they’re wrong and can be easily reversed. High-risk decisions can have significant financial, operational, or reputational impacts if they’re wrong. They are also harder to course-correct.\n\nMaking a decision also involves three components:\n\n**Framing**, which defines the problem, assumptions, stakeholders, constraints, and criteria for success.\n\n**Acting**, which is the process of gathering information, evaluating proposed options, recommending and authorizing actions, and executing approved steps.\n\n**Learning**, which is when outcomes are monitored and analyzed, there’s accountability for outcomes, and the decision system is updated over time.\n\n## Fine-tuning human and AI interactions\n\nConsidering both high and low levels of ambiguity and risk results in a matrix of four decision types.” Each decision type suggests a different role for AI in the three components of decision-making. Companies should assign decision-making activities to humans and AI accordingly while maintaining human oversight across all of them.\n\nThe researchers used One New Zealand, a telecommunications provider with more than 50 AI agents in operation as of early 2026, to illustrate how decision rights can be designed.\n\n**Routine decisions (low ambiguity, low risk).** Routine decisions are well defined and will have limited negative consequences if the wrong choice is made, which makes them strong candidates for automation. Companies can codify framing in advance and automate the action and learning aspects of the decision process. Humans should remain closely involved while confidence in agent performance builds.\n\n**Example:** One New Zealand developed a process for creating audience segments for targeted marketing campaigns. An AI agent translates marketer requests into SQL queries, reducing time spent on segmentation by 60%. The marketing team then reviews the AI-generated segmentation plan before launch, and the AI is granted more autonomy as confidence builds in its abilities.\n\n**Consequential decisions (low ambiguity, high risk).** These are well-defined decisions where potential errors can have significant negative consequences. Here, it’s important to balance automation and human intervention. Humans should continue to monitor execution, manage exceptions, and focus on continuous learning.\n\n**Example:** One New Zealand is using AI agents in customer service to facilitate plan upgrades and to support ticket resolution. Business leaders established the company’s risk tolerance to guide what’s acceptable for automation. For example, inaccurate pricing information was deemed a nonnegotiable constraint, so all decisions related to pricing require a human in the loop. Because the company focuses on continuous learning, agents are introduced gradually, and outcomes are validated before agents are released at scale.\n\n**Exploratory decisions (high ambiguity, low risk).** This class of decisions involves interpretation, creativity, or uncertainty, but errors will have limited consequences. Framing can evolve through human and AI interaction, with humans overseeing actions and continuous learning. Governance must be a priority as experimentation leads to higher-risk use cases.\n\n**Example:** One New Zealand’s marketing team employs agents for content creation tasks, maintaining humans in the loop to help frame goals and learn from customer responses. The team is now using what they have learned to develop agent-led, end-to-end autonomous marketing campaigns. The benefits: AI expands the team’s options and accelerates experimentation while human workers remain on hand to revise mistakes before they can have a major impact.\n\n**Strategic decisions (high ambiguity, high risk).** These decisions involve both uncertainty and significant potential consequences, which means that they require a great deal of human leadership and oversight. People need to take the lead in the framing and learning processes while AI facilitates action.\n\n**Example:** One New Zealand uses automation to optimize its network and improve resilience in the event of power outages. Fifteen task-based agents analyze network data. A set of orchestration agents manages those task-based agents while also providing data and analysis to support human decision-making. Actual decisions remain human-led because they involve trade-offs between service reliability, customer experience, cost, and infrastructure priorities.\n\n## Best practices for designing decision rights\n\nSeveral steps that One New Zealand has taken to facilitate decision rights design offer guidance for other companies. Among them:\n\n**Make organizational adjustments.** One New Zealand empowered business units with responsibility for framing each AI use case to ensure that agents are anchored in business need, value, and domain expertise. This also ensures that decision rights are developed and applied consistently.\n\n**Create an AI center of excellence.** The company’s center of excellence for AI and data has ownership of data foundations, architecture, and delivery. As part of its work, the center has established a unified data platform for structured and unstructured data; a horizontal AI orchestration layer to coordinate across agentic platforms; a responsible AI use policy; and an iterative rollout to support aligned decision-making.\n\n**Ensure continuous agent management.** Ongoing agent management is crucial to reducing risk. At One New Zealand, deployed AI agents must have a named human agent owner to ensure accountability for outcomes and learning. Human owners are responsible for monitoring performance, refining data, testing accuracy, and improving agents over time.\n\n**Manage AI as a portfolio of business decisions.** As AI is embedded in core processes, leaders need to look beyond individual use cases and consider the business decision that the AI implementation affects. Decision rights for routine and consequential decisions can deliver near-term efficiency gains and help with scale, while exploratory and strategic decisions can unlock longer-term value through innovation and better judgment.\n\n“Companies that get this right will move faster, reduce risk and build trust in how they use AI,” the authors write. “The important shift is from asking where AI can be used to manage the business decisions it shapes.”\n\n[Read the research: “Designing Decision Rights for AI”](https://cisr.mit.edu/publication/2026_0601_AIDecisionMatrix_SebastianWeillHaskampVomBrocke)\n\n---\n\n[***Ina Sebastian***](https://mitsloan.mit.edu/faculty/directory/ina-maria-sebastian) *is a research scientist at the* [*MIT Center for Information Systems Research*](https://cisr.mit.edu/)*. Her work focuses on how organizations create value from AI and other digital technologies, including AI-enabled business models, AI decision rights and trust, new organizational designs, and digital ecosystems.*\n\n[***Peter Weill***](https://mitsloan.mit.edu/faculty/directory/peter-weill) *is a senior research scientist at the MIT Sloan School of Management and chairman of MIT CISR. His work explores future trends, such as digital business models, IT investment portfolios, and AI maturity models, to help organizations maintain a competitive edge.*\n\n[***Thomas Haskamp***](https://www.wi.uni-muenster.de/department/bpm/people/thomas-haskamp) *is an academic research fellow at MIT CISR and an assistant professor in the department of information systems at the University of Münster.*\n\n[***Jan vom Brocke***](https://janvombrocke.com/) *is an academic research fellow at MIT CISR, a professor and chair of information systems and business process management at the University of Münster, and director of the European Research Center for Information Systems.*", "key_points": [ "Modern enterprises face the daunting challenge of determining what tasks artificial intelligence can safely perform without human oversight.", "The AI Decision Matrix can help business leaders assess ambiguity and risk when granting decision rights to humans and AI.", "Routine decisions, which have low ambiguity and low risk, are strong candidates for automation, whereas strategic decisions, which have high ambiguity and high risk, need a strong human lead. Human oversight remains important across all categories of decisions." ], "observed_at_ms": 1792693914675, "published_at": "2026-09-22T09:24:51-04:00", "read_time_minutes": 5, "slug": "a-framework-determining-when-ai-can-make-decisions", "summary": "The AI Decision Matrix uses ambiguity and risk to determine the optimal decision-making structure between humans and AI automation.", "title": "A framework for determining when AI can make decisions", "topic": { "name": "Artificial Intelligence", "slug": "artificial-intelligence" }, "updated_at": "2026-09-22T09:59:38-04:00", "url": "https://mitsloan.mit.edu/ideas-made-to-matter/a-framework-determining-when-ai-can-make-decisions", "word_count": 1263 } ``` ### List news - Capability: `news/list_news` - Description: Latest MIT News (news.mit.edu) stories from the site's official RSS feeds: the all-news feed, the campus or research section, one of the five schools, or any MIT News topic (artificial-intelligence2, climate-change-and-sustainability, physics, ...). Each item has title, URL, publication time, writer and office, and the story dek. Feeds hold the 50 newest stories; for older stories pass page to read the topic archive 15 at a time. q keeps only items whose title, dek or writer contain every word. Accepts a pasted topic page or feed URL. - Instructions: To find recent MIT research and campus news on a subject, e.g. the newest MIT AI or quantum stories, then read one with get_news_article. - Cost: 5 credits per call - Capability file: [List news](https://firecrawl.dev/alexandria/agents/providers/mit-edu/news/list_news) Accepted options: - `limit` (number): Items to return, newest first (default 20). Example: `10` - `page` (number): Read page N of the topic's archive (15 stories per page, 0 newest, as news.mit.edu/topic/?page=N) instead of its RSS feed. Needs topic or a topic url. Example: `10` - `q` (string): Keep items whose title, dek or writer contain every word (case-insensitive). Example: `` - `school` (string): One MIT school's feed. Example: `architecture-and-planning` - `section` (string): all (default), campus or research. Example: `all` - `topic` (string): MIT News topic slug as in news.mit.edu/topic/, e.g. artificial-intelligence2, quantum-computing, climate-change-and-sustainability. Unknown topics are not_found. Pattern: ^[A-Za-z0-9-]+$. Example: `` - `url` (string): A news.mit.edu topic page (https://news.mit.edu/topic/artificial-intelligence2, optionally ?page=N), an RSS feed URL (https://news.mit.edu/rss/...) or the home page. Replaces topic, school and section. Pattern: ^https?://. Example: `` Response schema example: ```json { "feed": "topic/artificial-intelligence2", "feed_title": "MIT News - Artificial intelligence", "feed_url": "https://news.mit.edu/rss/topic/artificial-intelligence2", "items": [ { "author": "Alex Ouyang", "author_affiliation": "Abdul Latif Jameel Clinic for Machine Learning in Health", "published_at": "2026-10-19T14:20:00-04:00", "summary": "A model developed by researchers at MIT and Boston Children's Hospital adjusts scans on the fly to track fetal movement, potentially sparing families repeat hospital visits.", "title": "Why teaching AI about symmetry could make prenatal diagnosis more reliable", "url": "https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019" }, { "author": "Zach Winn", "author_affiliation": "MIT News", "published_at": "2026-10-18T00:00:00-04:00", "summary": "New research can help AI models understand crystalline materials in 3D and reason about their properties.", "title": "Giving AI a “crystal clear” view of the materials world", "url": "https://news.mit.edu/2026/giving-ai-crystal-clear-view-of-materials-world-1018" }, { "author": "Zach Winn", "author_affiliation": "MIT News", "published_at": "2026-10-16T12:00:00-04:00", "summary": "The 18th annual Kendall Square Association Annual Meeting explored how new technologies could be a force for shared prosperity in society.", "title": "Fostering community amid Kendall Square’s innovation", "url": "https://news.mit.edu/2026/fostering-community-amid-kendall-square-innovation-1016" }, { "author": "Mackenzie Berry", "author_affiliation": "MIT Center for Transportation and Logistics", "published_at": "2026-10-15T17:00:00-04:00", "summary": "MIT's Center for Transportation and Logistics finds sustainability commitments are strong, but governance gaps and external disruptions prevent execution.", "title": "State of Supply Chain Sustainability Report finds corporate commitment at record high, but governance lags behind", "url": "https://news.mit.edu/2026/state-of-supply-chain-sustainability-report-1015" }, { "author": "Shafaq Zia", "author_affiliation": "Whitehead Institute", "published_at": "2026-10-15T16:50:00-04:00", "summary": "A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.", "title": "AI model decodes the language cells use to communicate", "url": "https://news.mit.edu/2026/ai-model-decodes-language-cells-use-communicate-1015" } ], "matched": 8, "next_page": null, "observed_at_ms": 1792693914635, "page": null, "q": null } ``` ### List sloan articles - Capability: `news/list_sloan_articles` - Description: The latest MIT Sloan 'Ideas Made to Matter' articles (mitsloan.mit.edu), from the hub or one topic page (artificial-intelligence, leadership, finance, work-careers, ...): title, URL, slug, primary topic, writers, summary and date for the ten newest, as the site lists them. - Instructions: To see what MIT Sloan has recently published on a management or business topic, then read one with get_sloan_article. - Cost: 5 credits per call - Capability file: [List sloan articles](https://firecrawl.dev/alexandria/agents/providers/mit-edu/news/list_sloan_articles) Accepted options: - `topic` (string): Topic slug as in mitsloan.mit.edu/ideas-made-to-matter/topics/, e.g. artificial-intelligence. Omit for the hub. Unknown topics are not_found. Pattern: ^[A-Za-z0-9-]+$. Example: `` - `url` (string): The hub https://mitsloan.mit.edu/ideas-made-to-matter or a topic page .../ideas-made-to-matter/topics/. Replaces topic. Pattern: ^https?://. Example: `` Response schema example: ```json { "articles": [ { "authors": [ "Beth Stackpole" ], "published_on": "2026-10-20", "slug": "how-to-level-systematic-innovation-ai", "summary": "Artificial intelligence can boost the systematic innovation of products, processes, and services. MIT Sloan professor Steven Eppinger offers four steps for making that happen.", "title": "How to level up systematic innovation with AI", "topic": { "name": "Innovation", "slug": "innovation" }, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/how-to-level-systematic-innovation-ai" }, { "authors": [ "Betsy Vereckey" ], "published_on": "2026-10-14", "slug": "ai-and-jobs-4-takeaways-googles-chief-economist", "summary": "Google’s Fabien Curto Millet examined how AI is reshaping job roles, workflows, and employee training in an address at the MIT Stone Center on Inequality and Shaping the Future of Work.", "title": "AI and jobs: 4 takeaways from Google’s chief economist", "topic": null, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/ai-and-jobs-4-takeaways-googles-chief-economist" }, { "authors": [ "Kristin Burnham" ], "published_on": "2026-10-13", "slug": "5-things-to-know-about-how-companies-can-create-value-ai", "summary": "As AI tools become more widely available, competitive advantage will depend on the trust, data, expertise, operating capabilities, and work redesign companies build around them.", "title": "5 things to know about how companies can create value from AI", "topic": { "name": "Artificial Intelligence", "slug": "artificial-intelligence" }, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/5-things-to-know-about-how-companies-can-create-value-ai" }, { "authors": [ "Sara Brown", "Tracy Mayor" ], "published_on": "2026-10-06", "slug": "these-new-mit-sloan-courses-track-emerging-technologies-and-business-trends", "summary": "MIT Sloan School of Management course offerings like AI-Augmented Work, Defense and Dual-Use Ventures, and Leading Agentic AI Transformation in Organizations anticipate businesses’ future needs.", "title": "These new MIT Sloan courses track emerging technologies and business trends", "topic": { "name": "Innovation", "slug": "innovation" }, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/these-new-mit-sloan-courses-track-emerging-technologies-and-business-trends" }, { "authors": [ "Kara Baskin" ], "published_on": "2026-10-05", "slug": "collision-what-ai-does-to-us", "summary": "In his new book, MIT Sloan School of Management professor Eric So examines the ways AI is reshaping human cognition and offers advice on protecting your uniquely human skills while using AI.", "title": "‘The Collision: What AI Does to Us’", "topic": { "name": "Artificial Intelligence", "slug": "artificial-intelligence" }, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/collision-what-ai-does-to-us" }, { "authors": [ "Dylan Walsh" ], "published_on": "2026-09-29", "slug": "4-seats-ai-can-occupy-negotiating-table", "summary": "AI can negotiate for you, coach you, support your team, or mediate between parties. An MIT Sloan School of Management professor explains those four roles — and why human judgment still matters.", "title": "4 seats AI can occupy at the negotiating table", "topic": { "name": "Negotiation", "slug": "negotiation" }, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/4-seats-ai-can-occupy-negotiating-table" }, { "authors": [ "Betsy Vereckey" ], "published_on": "2026-09-28", "slug": "what-employers-can-learn-kaiser-permanentes-ai-labor-deal", "summary": "An MIT report details how Kaiser Permanente and the Alliance of Health Care Unions reached a landmark labor agreement to incorporate worker input into AI decision-making.", "title": "Lessons from Kaiser Permanente’s AI labor deal", "topic": { "name": "Artificial Intelligence", "slug": "artificial-intelligence" }, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/what-employers-can-learn-kaiser-permanentes-ai-labor-deal" }, { "authors": [ "Beth Stackpole" ], "published_on": "2026-09-22", "slug": "a-framework-determining-when-ai-can-make-decisions", "summary": "The AI Decision Matrix uses ambiguity and risk to determine the optimal decision-making structure between humans and AI automation.", "title": "A framework for determining when AI can make decisions", "topic": { "name": "Artificial Intelligence", "slug": "artificial-intelligence" }, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/a-framework-determining-when-ai-can-make-decisions" }, { "authors": [ "Beth Stackpole" ], "published_on": "2026-09-21", "slug": "3-principles-to-bring-your-data-monetization-initiatives-to-life", "summary": "In today’s AI-driven environment, robust data monetization capabilities are both a competitive advantage and a precondition for success, research from the MIT Sloan School of Management finds.", "title": "3 principles to bring your data monetization initiatives to life", "topic": { "name": "Data", "slug": "data" }, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/3-principles-to-bring-your-data-monetization-initiatives-to-life" }, { "authors": [ "Kara Baskin" ], "published_on": "2026-09-15", "slug": "13-mit-startups-to-watch-2026", "summary": "The 2026 MIT delta v Demo Day showcased startup innovations in biotechnology, healthcare, AI, cybersecurity, robotics, finance, and manufacturing.", "title": "13 MIT startups to watch in 2026", "topic": { "name": "Entrepreneurship", "slug": "entrepreneurship" }, "url": "https://mitsloan.mit.edu/ideas-made-to-matter/13-mit-startups-to-watch-2026" } ], "listing_url": "https://mitsloan.mit.edu/ideas-made-to-matter/topics/artificial-intelligence", "observed_at_ms": 1792693914665, "topic": "artificial-intelligence" } ```