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      How machine learning models can amplify inequities in medical diagnosis and treatment

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      Prior to receiving a PhD in computer science from MIT in 2017, Marzyeh Ghassemi had already begun to wonder whether the use of AI techniques might enhance the biases that already existed in health care. She was one of the early researchers to take up this issue, and she’s been exploring it ever since. In a new paper, Ghassemi, now an assistant professor in MIT’s Department of Electrical Science and Engineering (EECS), and three collaborators based at the Computer Science and Artificial Intelligence Laboratory, have probed the roots of the disparities that can arise in machine learning, often causing models that perform well overall to falter when it comes to subgroups for which relatively few data have been collected and utilized in the training process. The paper — written by two MIT PhD students, Yuzhe Yang and Haoran Zhang, EECS computer scientist Dina Katabi (the Thuan and Nicole Pham Professor), and Ghassemi — was presented last month at the 40th International Conference on Machine Learning in Honolulu, Hawaii.

      In their analysis, the researchers focused on “subpopulation shifts” — differences in the way machine learning models perform for one subgroup as compared to another. “We want the models to be fair and work equally well for all groups, but instead we consistently observe the presence of shifts among different groups that can lead to inferior medical diagnosis and treatment,” says Yang, who along with Zhang are the two lead authors on the paper. The main point of their inquiry is to determine the kinds of subpopulation shifts that can occur and to uncover the mechanisms behind them so that, ultimately, more equitable models can be developed.

      The new paper “significantly advances our understanding” of the subpopulation shift phenomenon, claims Stanford University computer scientist Sanmi Koyejo. “This research contributes valuable insights for future advancements in machine learning models’ performance on underrepresented subgroups.”

      Camels and cattle

      The MIT group has identified four principal types of shifts — spurious correlations, attribute imbalance, class imbalance, and attribute generalization — which, according to Yang, “have never been put together into a coherent and unified framework. We’ve come up with a single equation that shows you where biases can come from.”

      Biases can, in fact, stem from what the researchers call the class, or from the attribute, or both. To pick a simple example, suppose the task assigned to the machine learning model is to sort images of objects — animals in this case — into two classes: cows and camels. Attributes are descriptors that don’t specifically relate to the class itself. It might turn out, for instance, that all the images used in the analysis show cows standing on grass and camels on sand — grass and sand serving as the attributes here. Given the data available to it, the machine could reach an erroneous conclusion — namely that cows can only be found on grass, not on sand, with the opposite being true for camels. Such a finding would be incorrect, however, giving rise to a spurious correlation, which, Yang explains, is a “special case” among subpopulation shifts — “one in which you have a bias in both the class and the attribute.”

      In a medical setting, one could rely on machine learning models to determine whether a person has pneumonia or not based on an examination of X-ray images. There would be two classes in this situation, one consisting of people who have the lung ailment, another for those who are infection-free. A relatively straightforward case would involve just two attributes: the people getting X-rayed are either female or male. If, in this particular dataset, there were 100 males diagnosed with pneumonia for every one female diagnosed with pneumonia, that could lead to an attribute imbalance, and the model would likely do a better job of correctly detecting pneumonia for a man than for a woman. Similarly, having 1,000 times more healthy (pneumonia-free) subjects than sick ones would lead to a class imbalance, with the model biased toward healthy cases. Attribute generalization is the last shift highlighted in the new study. If your sample contained 100 male patients with pneumonia and zero female subjects with the same illness, you still would like the model to be able to generalize and make predictions about female subjects even though there are no samples in the training data for females with pneumonia.

      The team then took 20 advanced algorithms, designed to carry out classification tasks, and tested them on a dozen datasets to see how they performed across different population groups. They reached some unexpected conclusions: By improving the “classifier,” which is the last layer of the neural network, they were able to reduce the occurrence of spurious correlations and class imbalance, but the other shifts were unaffected. Improvements to the “encoder,” one of the uppermost layers in the neural network, could reduce the problem of attribute imbalance. “However, no matter what we did to the encoder or classifier, we did not see any improvements in terms of attribute generalization,” Yang says, “and we don’t yet know how to address that.”

      Precisely accurate

      There is also the question of assessing how well your model actually works in terms of evenhandedness among different population groups. The metric normally used, called worst-group accuracy or WGA, is based on the assumption that if you can improve the accuracy — of, say, medical diagnosis — for the group that has the worst model performance, you would have improved the model as a whole. “The WGA is considered the gold standard in subpopulation evaluation,” the authors contend, but they made a surprising discovery: boosting worst-group accuracy results in a decrease in what they call “worst-case precision.” In medical decision-making of all sorts, one needs both accuracy — which speaks to the validity of the findings — and precision, which relates to the reliability of the methodology. “Precision and accuracy are both very important metrics in classification tasks, and that is especially true in medical diagnostics,” Yang explains. “You should never trade precision for accuracy. You always need to balance the two.”

      The MIT scientists are putting their theories into practice. In a study they’re conducting with a medical center, they’re looking at public datasets for tens of thousands of patients and hundreds of thousands of chest X-rays, trying to see whether it’s possible for machine learning models to work in an unbiased manner for all populations. That’s still far from the case, even though more awareness has been drawn to this problem, Yang says. “We are finding many disparities across different ages, gender, ethnicity, and intersectional groups.”

      He and his colleagues agree on the eventual goal, which is to achieve fairness in health care among all populations. But before we can reach that point, they maintain, we still need a better understanding of the sources of unfairness and how they permeate our current system. Reforming the system as a whole will not be easy, they acknowledge. In fact, the title of the paper they introduced at the Honolulu conference, “Change is Hard,” gives some indications as to the challenges that they and like-minded researchers face. More

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      Joining the battle against health care bias

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      Medical researchers are awash in a tsunami of clinical data. But we need major changes in how we gather, share, and apply this data to bring its benefits to all, says Leo Anthony Celi, principal research scientist at the MIT Laboratory for Computational Physiology (LCP). 

      One key change is to make clinical data of all kinds openly available, with the proper privacy safeguards, says Celi, a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC) in Boston. Another key is to fully exploit these open data with multidisciplinary collaborations among clinicians, academic investigators, and industry. A third key is to focus on the varying needs of populations across every country, and to empower the experts there to drive advances in treatment, says Celi, who is also an associate professor at Harvard Medical School. 

      In all of this work, researchers must actively seek to overcome the perennial problem of bias in understanding and applying medical knowledge. This deeply damaging problem is only heightened with the massive onslaught of machine learning and other artificial intelligence technologies. “Computers will pick up all our unconscious, implicit biases when we make decisions,” Celi warns.

      Play video

      Sharing medical data 

      Founded by the LCP, the MIT Critical Data consortium builds communities across disciplines to leverage the data that are routinely collected in the process of ICU care to understand health and disease better. “We connect people and align incentives,” Celi says. “In order to advance, hospitals need to work with universities, who need to work with industry partners, who need access to clinicians and data.” 

      The consortium’s flagship project is the MIMIC (medical information marked for intensive care) ICU database built at BIDMC. With about 35,000 users around the world, the MIMIC cohort is the most widely analyzed in critical care medicine. 

      International collaborations such as MIMIC highlight one of the biggest obstacles in health care: most clinical research is performed in rich countries, typically with most clinical trial participants being white males. “The findings of these trials are translated into treatment recommendations for every patient around the world,” says Celi. “We think that this is a major contributor to the sub-optimal outcomes that we see in the treatment of all sorts of diseases in Africa, in Asia, in Latin America.” 

      To fix this problem, “groups who are disproportionately burdened by disease should be setting the research agenda,” Celi says. 

      That’s the rule in the “datathons” (health hackathons) that MIT Critical Data has organized in more than two dozen countries, which apply the latest data science techniques to real-world health data. At the datathons, MIT students and faculty both learn from local experts and share their own skill sets. Many of these several-day events are sponsored by the MIT Industrial Liaison Program, the MIT International Science and Technology Initiatives program, or the MIT Sloan Latin America Office. 

      Datathons are typically held in that country’s national language or dialect, rather than English, with representation from academia, industry, government, and other stakeholders. Doctors, nurses, pharmacists, and social workers join up with computer science, engineering, and humanities students to brainstorm and analyze potential solutions. “They need each other’s expertise to fully leverage and discover and validate the knowledge that is encrypted in the data, and that will be translated into the way they deliver care,” says Celi. 

      “Everywhere we go, there is incredible talent that is completely capable of designing solutions to their health-care problems,” he emphasizes. The datathons aim to further empower the professionals and students in the host countries to drive medical research, innovation, and entrepreneurship.

      Play video

      Fighting built-in bias 

      Applying machine learning and other advanced data science techniques to medical data reveals that “bias exists in the data in unimaginable ways” in every type of health product, Celi says. Often this bias is rooted in the clinical trials required to approve medical devices and therapies. 

      One dramatic example comes from pulse oximeters, which provide readouts on oxygen levels in a patient’s blood. It turns out that these devices overestimate oxygen levels for people of color. “We have been under-treating individuals of color because the nurses and the doctors have been falsely assured that their patients have adequate oxygenation,” he says. “We think that we have harmed, if not killed, a lot of individuals in the past, especially during Covid, as a result of a technology that was not designed with inclusive test subjects.” 

      Such dangers only increase as the universe of medical data expands. “The data that we have available now for research is maybe two or three levels of magnitude more than what we had even 10 years ago,” Celi says. MIMIC, for example, now includes terabytes of X-ray, echocardiogram, and electrocardiogram data, all linked with related health records. Such enormous sets of data allow investigators to detect health patterns that were previously invisible. 

      “But there is a caveat,” Celi says. “It is trivial for computers to learn sensitive attributes that are not very obvious to human experts.” In a study released last year, for instance, he and his colleagues showed that algorithms can tell if a chest X-ray image belongs to a white patient or person of color, even without looking at any other clinical data. 

      “More concerningly, groups including ours have demonstrated that computers can learn easily if you’re rich or poor, just from your imaging alone,” Celi says. “We were able to train a computer to predict if you are on Medicaid, or if you have private insurance, if you feed them with chest X-rays without any abnormality. So again, computers are catching features that are not visible to the human eye.” And these features may lead algorithms to advise against therapies for people who are Black or poor, he says. 

      Opening up industry opportunities 

      Every stakeholder stands to benefit when pharmaceutical firms and other health-care corporations better understand societal needs and can target their treatments appropriately, Celi says. 

      “We need to bring to the table the vendors of electronic health records and the medical device manufacturers, as well as the pharmaceutical companies,” he explains. “They need to be more aware of the disparities in the way that they perform their research. They need to have more investigators representing underrepresented groups of people, to provide that lens to come up with better designs of health products.” 

      Corporations could benefit by sharing results from their clinical trials, and could immediately see these potential benefits by participating in datathons, Celi says. “They could really witness the magic that happens when that data is curated and analyzed by students and clinicians with different backgrounds from different countries. So we’re calling out our partners in the pharmaceutical industry to organize these events with us!”  More

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      Meet the 2022-23 Accenture Fellows

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      Launched in October 2020, the MIT and Accenture Convergence Initiative for Industry and Technology underscores the ways in which industry and technology can collaborate to spur innovation. The five-year initiative aims to achieve its mission through research, education, and fellowships. To that end, Accenture has once again awarded five annual fellowships to MIT graduate students working on research in industry and technology convergence who are underrepresented, including by race, ethnicity, and gender.This year’s Accenture Fellows work across research areas including telemonitoring, human-computer interactions, operations research,  AI-mediated socialization, and chemical transformations. Their research covers a wide array of projects, including designing low-power processing hardware for telehealth applications; applying machine learning to streamline and improve business operations; improving mental health care through artificial intelligence; and using machine learning to understand the environmental and health consequences of complex chemical reactions.As part of the application process, student nominations were invited from each unit within the School of Engineering, as well as from the Institute’s four other schools and the MIT Schwarzman College of Computing. Five exceptional students were selected as fellows for the initiative’s third year.Drew Buzzell is a doctoral candidate in electrical engineering and computer science whose research concerns telemonitoring, a fast-growing sphere of telehealth in which information is collected through internet-of-things (IoT) connected devices and transmitted to the cloud. Currently, the high volume of information involved in telemonitoring — and the time and energy costs of processing it — make data analysis difficult. Buzzell’s work is focused on edge computing, a new computing architecture that seeks to address these challenges by managing data closer to the source, in a distributed network of IoT devices. Buzzell earned his BS in physics and engineering science and his MS in engineering science from the Pennsylvania State University.

      Mengying (Cathy) Fang is a master’s student in the MIT School of Architecture and Planning. Her research focuses on augmented reality and virtual reality platforms. Fang is developing novel sensors and machine components that combine computation, materials science, and engineering. Moving forward, she will explore topics including soft robotics techniques that could be integrated with clothes and wearable devices and haptic feedback in order to develop interactions with digital objects. Fang earned a BS in mechanical engineering and human-computer interaction from Carnegie Mellon University.

      Xiaoyue Gong is a doctoral candidate in operations research at the MIT Sloan School of Management. Her research aims to harness the power of machine learning and data science to reduce inefficiencies in the operation of businesses, organizations, and society. With the support of an Accenture Fellowship, Gong seeks to find solutions to operational problems by designing reinforcement learning methods and other machine learning techniques to embedded operational problems. Gong earned a BS in honors mathematics and interactive media arts from New York University.

      Ruby Liu is a doctoral candidate in medical engineering and medical physics. Their research addresses the growing pandemic of loneliness among older adults, which leads to poor health outcomes and presents particularly high risks for historically marginalized people, including members of the LGBTQ+ community and people of color. Liu is designing a network of interconnected AI agents that foster connections between user and agent, offering mental health care while strengthening and facilitating human-human connections. Liu received a BS in biomedical engineering from Johns Hopkins University.

      Joules Provenzano is a doctoral candidate in chemical engineering. Their work integrates machine learning and liquid chromatography-high resolution mass spectrometry (LC-HRMS) to improve our understanding of complex chemical reactions in the environment. As an Accenture Fellow, Provenzano will build upon recent advances in machine learning and LC-HRMS, including novel algorithms for processing real, experimental HR-MS data and new approaches in extracting structure-transformation rules and kinetics. Their research could speed the pace of discovery in the chemical sciences and benefits industries including oil and gas, pharmaceuticals, and agriculture. Provenzano earned a BS in chemical engineering and international and global studies from the Rochester Institute of Technology. More

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      Large language models help decipher clinical notes

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      Electronic health records (EHRs) need a new public relations manager. Ten years ago, the U.S. government passed a law that required hospitals to digitize their health records with the intent of improving and streamlining care. The enormous amount of information in these now-digital records could be used to answer very specific questions beyond the scope of clinical trials: What’s the right dose of this medication for patients with this height and weight? What about patients with a specific genomic profile?

      Unfortunately, most of the data that could answer these questions is trapped in doctor’s notes, full of jargon and abbreviations. These notes are hard for computers to understand using current techniques — extracting information requires training multiple machine learning models. Models trained for one hospital, also, don’t work well at others, and training each model requires domain experts to label lots of data, a time-consuming and expensive process. 

      An ideal system would use a single model that can extract many types of information, work well at multiple hospitals, and learn from a small amount of labeled data. But how? Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) believed that to disentangle the data, they needed to call on something bigger: large language models. To pull that important medical information, they used a very big, GPT-3 style model to do tasks like expand overloaded jargon and acronyms and extract medication regimens. 

      For example, the system takes an input, which in this case is a clinical note, “prompts” the model with a question about the note, such as “expand this abbreviation, C-T-A.” The system returns an output such as “clear to auscultation,” as opposed to say, a CT angiography. The objective of extracting this clean data, the team says, is to eventually enable more personalized clinical recommendations. 

      Medical data is, understandably, a pretty tricky resource to navigate freely. There’s plenty of red tape around using public resources for testing the performance of large models because of data use restrictions, so the team decided to scrape together their own. Using a set of short, publicly available clinical snippets, they cobbled together a small dataset to enable evaluation of the extraction performance of large language models. 

      “It’s challenging to develop a single general-purpose clinical natural language processing system that will solve everyone’s needs and be robust to the huge variation seen across health datasets. As a result, until today, most clinical notes are not used in downstream analyses or for live decision support in electronic health records. These large language model approaches could potentially transform clinical natural language processing,” says David Sontag, MIT professor of electrical engineering and computer science, principal investigator in CSAIL and the Institute for Medical Engineering and Science, and supervising author on a paper about the work, which will be presented at the Conference on Empirical Methods in Natural Language Processing. “The research team’s advances in zero-shot clinical information extraction makes scaling possible. Even if you have hundreds of different use cases, no problem — you can build each model with a few minutes of work, versus having to label a ton of data for that particular task.”

      For example, without any labels at all, the researchers found these models could achieve 86 percent accuracy at expanding overloaded acronyms, and the team developed additional methods to boost this further to 90 percent accuracy, with still no labels required.

      Imprisoned in an EHR 

      Experts have been steadily building up large language models (LLMs) for quite some time, but they burst onto the mainstream with GPT-3’s widely covered ability to complete sentences. These LLMs are trained on a huge amount of text from the internet to finish sentences and predict the next most likely word. 

      While previous, smaller models like earlier GPT iterations or BERT have pulled off a good performance for extracting medical data, they still require substantial manual data-labeling effort. 

      For example, a note, “pt will dc vanco due to n/v” means that this patient (pt) was taking the antibiotic vancomycin (vanco) but experienced nausea and vomiting (n/v) severe enough for the care team to discontinue (dc) the medication. The team’s research avoids the status quo of training separate machine learning models for each task (extracting medication, side effects from the record, disambiguating common abbreviations, etc). In addition to expanding abbreviations, they investigated four other tasks, including if the models could parse clinical trials and extract detail-rich medication regimens.  

      “Prior work has shown that these models are sensitive to the prompt’s precise phrasing. Part of our technical contribution is a way to format the prompt so that the model gives you outputs in the correct format,” says Hunter Lang, CSAIL PhD student and author on the paper. “For these extraction problems, there are structured output spaces. The output space is not just a string. It can be a list. It can be a quote from the original input. So there’s more structure than just free text. Part of our research contribution is encouraging the model to give you an output with the correct structure. That significantly cuts down on post-processing time.”

      The approach can’t be applied to out-of-the-box health data at a hospital: that requires sending private patient information across the open internet to an LLM provider like OpenAI. The authors showed that it’s possible to work around this by distilling the model into a smaller one that could be used on-site.

      The model — sometimes just like humans — is not always beholden to the truth. Here’s what a potential problem might look like: Let’s say you’re asking the reason why someone took medication. Without proper guardrails and checks, the model might just output the most common reason for that medication, if nothing is explicitly mentioned in the note. This led to the team’s efforts to force the model to extract more quotes from data and less free text.

      Future work for the team includes extending to languages other than English, creating additional methods for quantifying uncertainty in the model, and pulling off similar results with open-sourced models. 

      “Clinical information buried in unstructured clinical notes has unique challenges compared to general domain text mostly due to large use of acronyms, and inconsistent textual patterns used across different health care facilities,” says Sadid Hasan, AI lead at Microsoft and former executive director of AI at CVS Health, who was not involved in the research. “To this end, this work sets forth an interesting paradigm of leveraging the power of general domain large language models for several important zero-/few-shot clinical NLP tasks. Specifically, the proposed guided prompt design of LLMs to generate more structured outputs could lead to further developing smaller deployable models by iteratively utilizing the model generated pseudo-labels.”

      “AI has accelerated in the last five years to the point at which these large models can predict contextualized recommendations with benefits rippling out across a variety of domains such as suggesting novel drug formulations, understanding unstructured text, code recommendations or create works of art inspired by any number of human artists or styles,” says Parminder Bhatia, who was formerly Head of Machine Learning at AWS Health AI and is currently Head of ML for low-code applications leveraging large language models at AWS AI Labs. “One of the applications of these large models [the team has] recently launched is Amazon CodeWhisperer, which is [an] ML-powered coding companion that helps developers in building applications.”

      As part of the MIT Abdul Latif Jameel Clinic for Machine Learning in Health, Agrawal, Sontag, and Lang wrote the paper alongside Yoon Kim, MIT assistant professor and CSAIL principal investigator, and Stefan Hegselmann, a visiting PhD student from the University of Muenster. First-author Agrawal’s research was supported by a Takeda Fellowship, the MIT Deshpande Center for Technological Innovation, and the MLA@CSAIL Initiatives. More

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      The promise and pitfalls of artificial intelligence explored at TEDxMIT event

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      Scientists, students, and community members came together last month to discuss the promise and pitfalls of artificial intelligence at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT. 

      Attendees were entertained and challenged as they explored “the good and bad of computing,” explained CSAIL Director Professor Daniela Rus, who organized the event with John Werner, an MIT fellow and managing director of Link Ventures; MIT sophomore Lucy Zhao; and grad student Jessica Karaguesian. “As you listen to the talks today,” Rus told the audience, “consider how our world is made better by AI, and also our intrinsic responsibilities for ensuring that the technology is deployed for the greater good.”

      Rus mentioned some new capabilities that could be enabled by AI: an automated personal assistant that could monitor your sleep phases and wake you at the optimal time, as well as on-body sensors that monitor everything from your posture to your digestive system. “Intelligent assistance can help empower and augment our lives. But these intriguing possibilities should only be pursued if we can simultaneously resolve the challenges that these technologies bring,” said Rus. 

      The next speaker, CSAIL principal investigator and professor of electrical engineering and computer science Manolis Kellis, started off by suggesting what sounded like an unattainable goal — using AI to “put an end to evolution as we know it.” Looking at it from a computer science perspective, he said, what we call evolution is basically a brute force search. “You’re just exploring all of the search space, creating billions of copies of every one of your programs, and just letting them fight against each other. This is just brutal. And it’s also completely slow. It took us billions of years to get here.” Might it be possible, he asked, to speed up evolution and make it less messy?

      The answer, Kellis said, is that we can do better, and that we’re already doing better: “We’re not killing people like Sparta used to, throwing the weaklings off the mountain. We are truly saving diversity.”

      Knowledge, moreover, is now being widely shared, passed on “horizontally” through accessible information sources, he noted, rather than “vertically,” from parent to offspring. “I would like to argue that competition in the human species has been replaced by collaboration. Despite having a fixed cognitive hardware, we have software upgrades that are enabled by culture, by the 20 years that our children spend in school to fill their brains with everything that humanity has learned, regardless of which family came up with it. This is the secret of our great acceleration” — the fact that human advancement in recent centuries has vastly out-clipped evolution’s sluggish pace.

      The next step, Kellis said, is to harness insights about evolution in order to combat an individual’s genetic susceptibility to disease. “Our current approach is simply insufficient,” he added. “We’re treating manifestations of disease, not the causes of disease.” A key element in his lab’s ambitious strategy to transform medicine is to identify “the causal pathways through which genetic predisposition manifests. It’s only by understanding these pathways that we can truly manipulate disease causation and reverse the disease circuitry.” 

      Kellis was followed by Aleksander Madry, MIT professor of electrical engineering and computer science and CSAIL principal investigator, who told the crowd, “progress in AI is happening, and it’s happening fast.” Computer programs can routinely beat humans in games like chess, poker, and Go. So should we be worried about AI surpassing humans? 

      Madry, for one, is not afraid — or at least not yet. And some of that reassurance stems from research that has led him to the following conclusion: Despite its considerable success, AI, especially in the form of machine learning, is lazy. “Think about being lazy as this kind of smart student who doesn’t really want to study for an exam. Instead, what he does is just study all the past years’ exams and just look for patterns. Instead of trying to actually learn, he just tries to pass the test. And this is exactly the same way in which current AI is lazy.”

      A machine-learning model might recognize grazing sheep, for instance, simply by picking out pictures that have green grass in them. If a model is trained to identify fish from photos of anglers proudly displaying their catches, Madry explained, “the model figures out that if there’s a human holding something in the picture, I will just classify it as a fish.” The consequences can be more serious for an AI model intended to pick out malignant tumors. If the model is trained on images containing rulers that indicate the size of tumors, the model may end up selecting only those photos that have rulers in them.

      This leads to Madry’s biggest concerns about AI in its present form. “AI is beating us now,” he noted. “But the way it does it [involves] a little bit of cheating.” He fears that we will apply AI “in some way in which this mismatch between what the model actually does versus what we think it does will have some catastrophic consequences.” People relying on AI, especially in potentially life-or-death situations, need to be much more mindful of its current limitations, Madry cautioned.

      There were 10 speakers altogether, and the last to take the stage was MIT associate professor of electrical engineering and computer science and CSAIL principal investigator Marzyeh Ghassemi, who laid out her vision for how AI could best contribute to general health and well-being. But in order for that to happen, its models must be trained on accurate, diverse, and unbiased medical data.

      It’s important to focus on the data, Ghassemi stressed, because these models are learning from us. “Since our data is human-generated … a neural network is learning how to practice from a doctor. But doctors are human, and humans make mistakes. And if a human makes a mistake, and we train an AI from that, the AI will, too. Garbage in, garbage out. But it’s not like the garbage is distributed equally.”

      She pointed out that many subgroups receive worse care from medical practitioners, and members of these subgroups die from certain conditions at disproportionately high rates. This is an area, Ghassemi said, “where AI can actually help. This is something we can fix.” Her group is developing machine-learning models that are robust, private, and fair. What’s holding them back is neither algorithms nor GPUs. It’s data. Once we collect reliable data from diverse sources, Ghassemi added, we might start reaping the benefits that AI can bring to the realm of health care.

      In addition to CSAIL speakers, there were talks from members across MIT’s Institute for Data, Systems, and Society; the MIT Mobility Initiative; the MIT Media Lab; and the SENSEable City Lab.

      The proceedings concluded on that hopeful note. Rus and Werner then thanked everyone for coming. “Please continue to reflect about the good and bad of computing,” Rus urged. “And we look forward to seeing you back here in May for the next TEDxMIT event.”

      The exact theme of the spring 2022 gathering will have something to do with “superpowers.” But — if December’s mind-bending presentations were any indication — the May offering is almost certain to give its attendees plenty to think about. And maybe provide the inspiration for a startup or two. More