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      A technique to improve both fairness and accuracy in artificial intelligence

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      For workers who use machine-learning models to help them make decisions, knowing when to trust a model’s predictions is not always an easy task, especially since these models are often so complex that their inner workings remain a mystery.

      Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence level for each prediction and will reject predictions when its confidence is too low. Then a human can examine those cases, gather additional information, and make a decision about each one manually.

      But while selective regression has been shown to improve the overall performance of a model, researchers at MIT and the MIT-IBM Watson AI Lab have discovered that the technique can have the opposite effect for underrepresented groups of people in a dataset. As the model’s confidence increases with selective regression, its chance of making the right prediction also increases, but this does not always happen for all subgroups.

      For instance, a model suggesting loan approvals might make fewer errors on average, but it may actually make more wrong predictions for Black or female applicants. One reason this can occur is due to the fact that the model’s confidence measure is trained using overrepresented groups and may not be accurate for these underrepresented groups.

      Once they had identified this problem, the MIT researchers developed two algorithms that can remedy the issue. Using real-world datasets, they show that the algorithms reduce performance disparities that had affected marginalized subgroups.

      “Ultimately, this is about being more intelligent about which samples you hand off to a human to deal with. Rather than just minimizing some broad error rate for the model, we want to make sure the error rate across groups is taken into account in a smart way,” says senior MIT author Greg Wornell, the Sumitomo Professor in Engineering in the Department of Electrical Engineering and Computer Science (EECS) who leads the Signals, Information, and Algorithms Laboratory in the Research Laboratory of Electronics (RLE) and is a member of the MIT-IBM Watson AI Lab.

      Joining Wornell on the paper are co-lead authors Abhin Shah, an EECS graduate student, and Yuheng Bu, a postdoc in RLE; as well as Joshua Ka-Wing Lee SM ’17, ScD ’21 and Subhro Das, Rameswar Panda, and Prasanna Sattigeri, research staff members at the MIT-IBM Watson AI Lab. The paper will be presented this month at the International Conference on Machine Learning.

      To predict or not to predict

      Regression is a technique that estimates the relationship between a dependent variable and independent variables. In machine learning, regression analysis is commonly used for prediction tasks, such as predicting the price of a home given its features (number of bedrooms, square footage, etc.) With selective regression, the machine-learning model can make one of two choices for each input — it can make a prediction or abstain from a prediction if it doesn’t have enough confidence in its decision.

      When the model abstains, it reduces the fraction of samples it is making predictions on, which is known as coverage. By only making predictions on inputs that it is highly confident about, the overall performance of the model should improve. But this can also amplify biases that exist in a dataset, which occur when the model does not have sufficient data from certain subgroups. This can lead to errors or bad predictions for underrepresented individuals.

      The MIT researchers aimed to ensure that, as the overall error rate for the model improves with selective regression, the performance for every subgroup also improves. They call this monotonic selective risk.

      “It was challenging to come up with the right notion of fairness for this particular problem. But by enforcing this criteria, monotonic selective risk, we can make sure the model performance is actually getting better across all subgroups when you reduce the coverage,” says Shah.

      Focus on fairness

      The team developed two neural network algorithms that impose this fairness criteria to solve the problem.

      One algorithm guarantees that the features the model uses to make predictions contain all information about the sensitive attributes in the dataset, such as race and sex, that is relevant to the target variable of interest. Sensitive attributes are features that may not be used for decisions, often due to laws or organizational policies. The second algorithm employs a calibration technique to ensure the model makes the same prediction for an input, regardless of whether any sensitive attributes are added to that input.

      The researchers tested these algorithms by applying them to real-world datasets that could be used in high-stakes decision making. One, an insurance dataset, is used to predict total annual medical expenses charged to patients using demographic statistics; another, a crime dataset, is used to predict the number of violent crimes in communities using socioeconomic information. Both datasets contain sensitive attributes for individuals.

      When they implemented their algorithms on top of a standard machine-learning method for selective regression, they were able to reduce disparities by achieving lower error rates for the minority subgroups in each dataset. Moreover, this was accomplished without significantly impacting the overall error rate.

      “We see that if we don’t impose certain constraints, in cases where the model is really confident, it could actually be making more errors, which could be very costly in some applications, like health care. So if we reverse the trend and make it more intuitive, we will catch a lot of these errors. A major goal of this work is to avoid errors going silently undetected,” Sattigeri says.

      The researchers plan to apply their solutions to other applications, such as predicting house prices, student GPA, or loan interest rate, to see if the algorithms need to be calibrated for those tasks, says Shah. They also want to explore techniques that use less sensitive information during the model training process to avoid privacy issues.

      And they hope to improve the confidence estimates in selective regression to prevent situations where the model’s confidence is low, but its prediction is correct. This could reduce the workload on humans and further streamline the decision-making process, Sattigeri says.

      This research was funded, in part, by the MIT-IBM Watson AI Lab and its member companies Boston Scientific, Samsung, and Wells Fargo, and by the National Science Foundation. More

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      Exploring emerging topics in artificial intelligence policy

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      Members of the public sector, private sector, and academia convened for the second AI Policy Forum Symposium last month to explore critical directions and questions posed by artificial intelligence in our economies and societies.

      The virtual event, hosted by the AI Policy Forum (AIPF) — an undertaking by the MIT Schwarzman College of Computing to bridge high-level principles of AI policy with the practices and trade-offs of governing — brought together an array of distinguished panelists to delve into four cross-cutting topics: law, auditing, health care, and mobility.

      In the last year there have been substantial changes in the regulatory and policy landscape around AI in several countries — most notably in Europe with the development of the European Union Artificial Intelligence Act, the first attempt by a major regulator to propose a law on artificial intelligence. In the United States, the National AI Initiative Act of 2020, which became law in January 2021, is providing a coordinated program across federal government to accelerate AI research and application for economic prosperity and security gains. Finally, China recently advanced several new regulations of its own.

      Each of these developments represents a different approach to legislating AI, but what makes a good AI law? And when should AI legislation be based on binding rules with penalties versus establishing voluntary guidelines?

      Jonathan Zittrain, professor of international law at Harvard Law School and director of the Berkman Klein Center for Internet and Society, says the self-regulatory approach taken during the expansion of the internet had its limitations with companies struggling to balance their interests with those of their industry and the public.

      “One lesson might be that actually having representative government take an active role early on is a good idea,” he says. “It’s just that they’re challenged by the fact that there appears to be two phases in this environment of regulation. One, too early to tell, and two, too late to do anything about it. In AI I think a lot of people would say we’re still in the ‘too early to tell’ stage but given that there’s no middle zone before it’s too late, it might still call for some regulation.”

      A theme that came up repeatedly throughout the first panel on AI laws — a conversation moderated by Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and chair of the AI Policy Forum — was the notion of trust. “If you told me the truth consistently, I would say you are an honest person. If AI could provide something similar, something that I can say is consistent and is the same, then I would say it’s trusted AI,” says Bitange Ndemo, professor of entrepreneurship at the University of Nairobi and the former permanent secretary of Kenya’s Ministry of Information and Communication.

      Eva Kaili, vice president of the European Parliament, adds that “In Europe, whenever you use something, like any medication, you know that it has been checked. You know you can trust it. You know the controls are there. We have to achieve the same with AI.” Kalli further stresses that building trust in AI systems will not only lead to people using more applications in a safe manner, but that AI itself will reap benefits as greater amounts of data will be generated as a result.

      The rapidly increasing applicability of AI across fields has prompted the need to address both the opportunities and challenges of emerging technologies and the impact they have on social and ethical issues such as privacy, fairness, bias, transparency, and accountability. In health care, for example, new techniques in machine learning have shown enormous promise for improving quality and efficiency, but questions of equity, data access and privacy, safety and reliability, and immunology and global health surveillance remain at large.

      MIT’s Marzyeh Ghassemi, an assistant professor in the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, and David Sontag, an associate professor of electrical engineering and computer science, collaborated with Ziad Obermeyer, an associate professor of health policy and management at the University of California Berkeley School of Public Health, to organize AIPF Health Wide Reach, a series of sessions to discuss issues of data sharing and privacy in clinical AI. The organizers assembled experts devoted to AI, policy, and health from around the world with the goal of understanding what can be done to decrease barriers to access to high-quality health data to advance more innovative, robust, and inclusive research results while being respectful of patient privacy.

      Over the course of the series, members of the group presented on a topic of expertise and were tasked with proposing concrete policy approaches to the challenge discussed. Drawing on these wide-ranging conversations, participants unveiled their findings during the symposium, covering nonprofit and government success stories and limited access models; upside demonstrations; legal frameworks, regulation, and funding; technical approaches to privacy; and infrastructure and data sharing. The group then discussed some of their recommendations that are summarized in a report that will be released soon.

      One of the findings calls for the need to make more data available for research use. Recommendations that stem from this finding include updating regulations to promote data sharing to enable easier access to safe harbors such as the Health Insurance Portability and Accountability Act (HIPAA) has for de-identification, as well as expanding funding for private health institutions to curate datasets, amongst others. Another finding, to remove barriers to data for researchers, supports a recommendation to decrease obstacles to research and development on federally created health data. “If this is data that should be accessible because it’s funded by some federal entity, we should easily establish the steps that are going to be part of gaining access to that so that it’s a more inclusive and equitable set of research opportunities for all,” says Ghassemi. The group also recommends taking a careful look at the ethical principles that govern data sharing. While there are already many principles proposed around this, Ghassemi says that “obviously you can’t satisfy all levers or buttons at once, but we think that this is a trade-off that’s very important to think through intelligently.”

      In addition to law and health care, other facets of AI policy explored during the event included auditing and monitoring AI systems at scale, and the role AI plays in mobility and the range of technical, business, and policy challenges for autonomous vehicles in particular.

      The AI Policy Forum Symposium was an effort to bring together communities of practice with the shared aim of designing the next chapter of AI. In his closing remarks, Aleksander Madry, the Cadence Designs Systems Professor of Computing at MIT and faculty co-lead of the AI Policy Forum, emphasized the importance of collaboration and the need for different communities to communicate with each other in order to truly make an impact in the AI policy space.

      “The dream here is that we all can meet together — researchers, industry, policymakers, and other stakeholders — and really talk to each other, understand each other’s concerns, and think together about solutions,” Madry said. “This is the mission of the AI Policy Forum and this is what we want to enable.” More

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      3 Questions: Designing software for research ethics

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      Data are arguably the world’s hottest form of currency, clocking in zeros and ones that hold ever more weight than before. But with all of our personal information being crunched into dynamite for enterprise solutions and the like, with a lack of consumer data protection, are we all getting left behind? 

      Jonathan Zong, a PhD candidate in electrical engineering and computer science at MIT, and an affiliate of the Computer Science and Artificial Intelligence Laboratory, thinks consent can be baked into the design of the software that gathers our data for online research. He created Bartleby, a system for debriefing research participants and eliciting their views about social media research that involved them. Using Bartleby, he says, researchers can automatically direct each of their study participants to a website where they can learn about their involvement in research, view what data researchers collected about them, and give feedback. Most importantly, participants can use the website to opt out and request to delete their data.  

      Zong and his co-author, Nathan Matias SM ’13, PhD ’17, evaluated Bartleby by debriefing thousands of participants in observational and experimental studies on Twitter and Reddit. They found that Bartleby addresses procedural concerns by creating opportunities for participants to exercise autonomy, and the tool enabled substantive, value-driven conversations about participant voice and power. Here, Zong discusses the implications of their recent work as well as the future of social, ethical, and responsible computing.

      Q: Many leading tech ethicists and policymakers believe it’s impossible to keep people informed about their involvement in research and how their data are used. How has your work changed that?

      A: When Congress asked Mark Zuckerberg in 2018 about Facebook’s obligations to keep users informed about how their data is used, his answer was effectively that all users had the opportunity to read the privacy policy, and that being any clearer would be too difficult. Tech elites often blanket-statement that ethics is complicated, and proceed with their objective anyway. Many have claimed it’s impossible to fulfill ethical responsibilities to users at scale, so why try? But by creating Bartleby, a system for debriefing participants and eliciting their views about studies that involved them, we built something that shows that it’s not only very possible, but actually pretty easy to do. In a lot of situations, letting people know we want their data and explaining why we think it’s worth it is the bare minimum we could be doing.

      Q: Can ethical challenges be solved with a software tool?

      A: Off-the-shelf software actually can make a meaningful difference in respecting people’s autonomy. Ethics regulations almost never require a debriefing process for online studies. But because we used Bartleby, people had a chance to make an informed decision. It’s a chance they otherwise wouldn’t have had.

      At the same time, we realized that using Bartleby shined a light on deeper ethics questions that required substantive reflection. For example, most people are just trying to go about their lives and ignore the messages we send them, while others reply with concerns that aren’t even always about the research. Even if indirectly, these instances help signal nuances that research participants care about.

      Where might our values as researchers differ from participants’ values? How do the power structures that shape researchers’ interaction with users and communities affect our ability to see those differences? Using software to deliver ethics procedures helps bring these questions to light. But rather than expecting definitive answers that work in every situation, we should be thinking about how using software to create opportunities for participant voice and power challenges and invites us to reflect on how we address conflicting values.

      Q: How does your approach to design help suggest a way forward for social, ethical, and responsible computing?

      A: In addition to presenting the software tool, our peer-reviewed article on Bartleby also demonstrates a theoretical framework for data ethics, inspired by ideas in feminist philosophy. Because my work spans software design, empirical social science, and philosophy, I often think about the things I want people to take away in terms of interdisciplinary bridges I want to build. 

      I hope people look at Bartleby and see that ethics is an exciting area for technical innovation that can be tested empirically — guided by a clear-headed understanding of values. Umberto Eco, a philosopher, wrote that “form must not be a vehicle for thought, it must be a way of thinking.” In other words, designing software isn’t just about putting ideas we’ve already had into a computational form. Design is also a way we can think new ideas into existence, produce new ways of knowing and doing, and imagine alternative futures. More

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      How artificial intelligence can help combat systemic racism

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      In 2020, Detroit police arrested a Black man for shoplifting almost $4,000 worth of watches from an upscale boutique. He was handcuffed in front of his family and spent a night in lockup. After some questioning, however, it became clear that they had the wrong man. So why did they arrest him in the first place?

      The reason: a facial recognition algorithm had matched the photo on his driver’s license to grainy security camera footage.

      Facial recognition algorithms — which have repeatedly been demonstrated to be less accurate for people with darker skin — are just one example of how racial bias gets replicated within and perpetuated by emerging technologies.

      “There’s an urgency as AI is used to make really high-stakes decisions,” says MLK Visiting Professor S. Craig Watkins, whose academic home for his time at MIT is the Institute for Data, Systems, and Society (IDSS). “The stakes are higher because new systems can replicate historical biases at scale.”

      Watkins, a professor at the University of Texas at Austin and the founding director of the Institute for Media Innovation​, researches the impacts of media and data-based systems on human behavior, with a specific concentration on issues related to systemic racism. “One of the fundamental questions of the work is: how do we build AI models that deal with systemic inequality more effectively?”

      Play video

      Artificial Intelligence and the Future of Racial Justice | S. Craig Watkins | TEDxMIT

      Ethical AI

      Inequality is perpetuated by technology in many ways across many sectors. One broad domain is health care, where Watkins says inequity shows up in both quality of and access to care. The demand for mental health care, for example, far outstrips the capacity for services in the United States. That demand has been exacerbated by the pandemic, and access to care is harder for communities of color.

      For Watkins, taking the bias out of the algorithm is just one component of building more ethical AI. He works also to develop tools and platforms that can address inequality outside of tech head-on. In the case of mental health access, this entails developing a tool to help mental health providers deliver care more efficiently.

      “We are building a real-time data collection platform that looks at activities and behaviors and tries to identify patterns and contexts in which certain mental states emerge,” says Watkins. “The goal is to provide data-informed insights to care providers in order to deliver higher-impact services.”

      Watkins is no stranger to the privacy concerns such an app would raise. He takes a user-centered approach to the development that is grounded in data ethics. “Data rights are a significant component,” he argues. “You have to give the user complete control over how their data is shared and used and what data a care provider sees. No one else has access.”

      Combating systemic racism

      Here at MIT, Watkins has joined the newly launched Initiative on Combatting Systemic Racism (ICSR), an IDSS research collaboration that brings together faculty and researchers from the MIT Stephen A. Schwarzman College of Computing and beyond. The aim of the ICSR is to develop and harness computational tools that can help effect structural and normative change toward racial equity.

      The ICSR collaboration has separate project teams researching systemic racism in different sectors of society, including health care. Each of these “verticals” addresses different but interconnected issues, from sustainability to employment to gaming. Watkins is a part of two ICSR groups, policing and housing, that aim to better understand the processes that lead to discriminatory practices in both sectors. “Discrimination in housing contributes significantly to the racial wealth gap in the U.S.,” says Watkins.

      The policing team examines patterns in how different populations get policed. “There is obviously a significant and charged history to policing and race in America,” says Watkins. “This is an attempt to understand, to identify patterns, and note regional differences.”

      Watkins and the policing team are building models using data that details police interventions, responses, and race, among other variables. The ICSR is a good fit for this kind of research, says Watkins, who notes the interdisciplinary focus of both IDSS and the SCC. 

      “Systemic change requires a collaborative model and different expertise,” says Watkins. “We are trying to maximize influence and potential on the computational side, but we won’t get there with computation alone.”

      Opportunities for change

      Models can also predict outcomes, but Watkins is careful to point out that no algorithm alone will solve racial challenges.

      “Models in my view can inform policy and strategy that we as humans have to create. Computational models can inform and generate knowledge, but that doesn’t equate with change.” It takes additional work — and additional expertise in policy and advocacy — to use knowledge and insights to strive toward progress.

      One important lever of change, he argues, will be building a more AI-literate society through access to information and opportunities to understand AI and its impact in a more dynamic way. He hopes to see greater data rights and greater understanding of how societal systems impact our lives.

      “I was inspired by the response of younger people to the murders of George Floyd and Breonna Taylor,” he says. “Their tragic deaths shine a bright light on the real-world implications of structural racism and has forced the broader society to pay more attention to this issue, which creates more opportunities for change.” More

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      Can machine-learning models overcome biased datasets?

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      Artificial intelligence systems may be able to complete tasks quickly, but that doesn’t mean they always do so fairly. If the datasets used to train machine-learning models contain biased data, it is likely the system could exhibit that same bias when it makes decisions in practice.

      For instance, if a dataset contains mostly images of white men, then a facial-recognition model trained with these data may be less accurate for women or people with different skin tones.

      A group of researchers at MIT, in collaboration with researchers at Harvard University and Fujitsu Ltd., sought to understand when and how a machine-learning model is capable of overcoming this kind of dataset bias. They used an approach from neuroscience to study how training data affects whether an artificial neural network can learn to recognize objects it has not seen before. A neural network is a machine-learning model that mimics the human brain in the way it contains layers of interconnected nodes, or “neurons,” that process data.

      The new results show that diversity in training data has a major influence on whether a neural network is able to overcome bias, but at the same time dataset diversity can degrade the network’s performance. They also show that how a neural network is trained, and the specific types of neurons that emerge during the training process, can play a major role in whether it is able to overcome a biased dataset.

      “A neural network can overcome dataset bias, which is encouraging. But the main takeaway here is that we need to take into account data diversity. We need to stop thinking that if you just collect a ton of raw data, that is going to get you somewhere. We need to be very careful about how we design datasets in the first place,” says Xavier Boix, a research scientist in the Department of Brain and Cognitive Sciences (BCS) and the Center for Brains, Minds, and Machines (CBMM), and senior author of the paper.  

      Co-authors include former MIT graduate students Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, and Spandan Madan, a corresponding author who is currently pursuing a PhD at Harvard; Tomotake Sasaki, a former visiting scientist now a senior researcher at Fujitsu Research; Frédo Durand, a professor of electrical engineering and computer science at MIT and a member of the Computer Science and Artificial Intelligence Laboratory; and Hanspeter Pfister, the An Wang Professor of Computer Science at the Harvard School of Enginering and Applied Sciences. The research appears today in Nature Machine Intelligence.

      Thinking like a neuroscientist

      Boix and his colleagues approached the problem of dataset bias by thinking like neuroscientists. In neuroscience, Boix explains, it is common to use controlled datasets in experiments, meaning a dataset in which the researchers know as much as possible about the information it contains.

      The team built datasets that contained images of different objects in varied poses, and carefully controlled the combinations so some datasets had more diversity than others. In this case, a dataset had less diversity if it contains more images that show objects from only one viewpoint. A more diverse dataset had more images showing objects from multiple viewpoints. Each dataset contained the same number of images.

      The researchers used these carefully constructed datasets to train a neural network for image classification, and then studied how well it was able to identify objects from viewpoints the network did not see during training (known as an out-of-distribution combination). 

      For example, if researchers are training a model to classify cars in images, they want the model to learn what different cars look like. But if every Ford Thunderbird in the training dataset is shown from the front, when the trained model is given an image of a Ford Thunderbird shot from the side, it may misclassify it, even if it was trained on millions of car photos.

      The researchers found that if the dataset is more diverse — if more images show objects from different viewpoints — the network is better able to generalize to new images or viewpoints. Data diversity is key to overcoming bias, Boix says.

      “But it is not like more data diversity is always better; there is a tension here. When the neural network gets better at recognizing new things it hasn’t seen, then it will become harder for it to recognize things it has already seen,” he says.

      Testing training methods

      The researchers also studied methods for training the neural network.

      In machine learning, it is common to train a network to perform multiple tasks at the same time. The idea is that if a relationship exists between the tasks, the network will learn to perform each one better if it learns them together.

      But the researchers found the opposite to be true — a model trained separately for each task was able to overcome bias far better than a model trained for both tasks together.

      “The results were really striking. In fact, the first time we did this experiment, we thought it was a bug. It took us several weeks to realize it was a real result because it was so unexpected,” he says.

      They dove deeper inside the neural networks to understand why this occurs.

      They found that neuron specialization seems to play a major role. When the neural network is trained to recognize objects in images, it appears that two types of neurons emerge — one that specializes in recognizing the object category and another that specializes in recognizing the viewpoint.

      When the network is trained to perform tasks separately, those specialized neurons are more prominent, Boix explains. But if a network is trained to do both tasks simultaneously, some neurons become diluted and don’t specialize for one task. These unspecialized neurons are more likely to get confused, he says.

      “But the next question now is, how did these neurons get there? You train the neural network and they emerge from the learning process. No one told the network to include these types of neurons in its architecture. That is the fascinating thing,” he says.

      That is one area the researchers hope to explore with future work. They want to see if they can force a neural network to develop neurons with this specialization. They also want to apply their approach to more complex tasks, such as objects with complicated textures or varied illuminations.

      Boix is encouraged that a neural network can learn to overcome bias, and he is hopeful their work can inspire others to be more thoughtful about the datasets they are using in AI applications.

      This work was supported, in part, by the National Science Foundation, a Google Faculty Research Award, the Toyota Research Institute, the Center for Brains, Minds, and Machines, Fujitsu Research, and the MIT-Sensetime Alliance on Artificial Intelligence. More

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      The downside of machine learning in health care

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      While working toward her dissertation in computer science at MIT, Marzyeh Ghassemi wrote several papers on how machine-learning techniques from artificial intelligence could be applied to clinical data in order to predict patient outcomes. “It wasn’t until the end of my PhD work that one of my committee members asked: ‘Did you ever check to see how well your model worked across different groups of people?’”

      That question was eye-opening for Ghassemi, who had previously assessed the performance of models in aggregate, across all patients. Upon a closer look, she saw that models often worked differently — specifically worse — for populations including Black women, a revelation that took her by surprise. “I hadn’t made the connection beforehand that health disparities would translate directly to model disparities,” she says. “And given that I am a visible minority woman-identifying computer scientist at MIT, I am reasonably certain that many others weren’t aware of this either.”

      In a paper published Jan. 14 in the journal Patterns, Ghassemi — who earned her doctorate in 2017 and is now an assistant professor in the Department of Electrical Engineering and Computer Science and the MIT Institute for Medical Engineering and Science (IMES) — and her coauthor, Elaine Okanyene Nsoesie of Boston University, offer a cautionary note about the prospects for AI in medicine. “If used carefully, this technology could improve performance in health care and potentially reduce inequities,” Ghassemi says. “But if we’re not actually careful, technology could worsen care.”

      It all comes down to data, given that the AI tools in question train themselves by processing and analyzing vast quantities of data. But the data they are given are produced by humans, who are fallible and whose judgments may be clouded by the fact that they interact differently with patients depending on their age, gender, and race, without even knowing it.

      Furthermore, there is still great uncertainty about medical conditions themselves. “Doctors trained at the same medical school for 10 years can, and often do, disagree about a patient’s diagnosis,” Ghassemi says. That’s different from the applications where existing machine-learning algorithms excel — like object-recognition tasks — because practically everyone in the world will agree that a dog is, in fact, a dog.

      Machine-learning algorithms have also fared well in mastering games like chess and Go, where both the rules and the “win conditions” are clearly defined. Physicians, however, don’t always concur on the rules for treating patients, and even the win condition of being “healthy” is not widely agreed upon. “Doctors know what it means to be sick,” Ghassemi explains, “and we have the most data for people when they are sickest. But we don’t get much data from people when they are healthy because they’re less likely to see doctors then.”

      Even mechanical devices can contribute to flawed data and disparities in treatment. Pulse oximeters, for example, which have been calibrated predominately on light-skinned individuals, do not accurately measure blood oxygen levels for people with darker skin. And these deficiencies are most acute when oxygen levels are low — precisely when accurate readings are most urgent. Similarly, women face increased risks during “metal-on-metal” hip replacements, Ghassemi and Nsoesie write, “due in part to anatomic differences that aren’t taken into account in implant design.” Facts like these could be buried within the data fed to computer models whose output will be undermined as a result.

      Coming from computers, the product of machine-learning algorithms offers “the sheen of objectivity,” according to Ghassemi. But that can be deceptive and dangerous, because it’s harder to ferret out the faulty data supplied en masse to a computer than it is to discount the recommendations of a single possibly inept (and maybe even racist) doctor. “The problem is not machine learning itself,” she insists. “It’s people. Human caregivers generate bad data sometimes because they are not perfect.”

      Nevertheless, she still believes that machine learning can offer benefits in health care in terms of more efficient and fairer recommendations and practices. One key to realizing the promise of machine learning in health care is to improve the quality of data, which is no easy task. “Imagine if we could take data from doctors that have the best performance and share that with other doctors that have less training and experience,” Ghassemi says. “We really need to collect this data and audit it.”

      The challenge here is that the collection of data is not incentivized or rewarded, she notes. “It’s not easy to get a grant for that, or ask students to spend time on it. And data providers might say, ‘Why should I give my data out for free when I can sell it to a company for millions?’ But researchers should be able to access data without having to deal with questions like: ‘What paper will I get my name on in exchange for giving you access to data that sits at my institution?’

      “The only way to get better health care is to get better data,” Ghassemi says, “and the only way to get better data is to incentivize its release.”

      It’s not only a question of collecting data. There’s also the matter of who will collect it and vet it. Ghassemi recommends assembling diverse groups of researchers — clinicians, statisticians, medical ethicists, and computer scientists — to first gather diverse patient data and then “focus on developing fair and equitable improvements in health care that can be deployed in not just one advanced medical setting, but in a wide range of medical settings.”

      The objective of the Patterns paper is not to discourage technologists from bringing their expertise in machine learning to the medical world, she says. “They just need to be cognizant of the gaps that appear in treatment and other complexities that ought to be considered before giving their stamp of approval to a particular computer model.” 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

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      in Data Management & Statistics

      Physics and the machine-learning “black box”

      by

      Machine-learning algorithms are often referred to as a “black box.” Once data are put into an algorithm, it’s not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the “black box” problem, through a combination of data science and physics-based engineering.

      In class 2.C161 (Physical Systems Modeling and Design Using Machine Learning), Professor George Barbastathis demonstrates how mechanical engineers can use their unique knowledge of physical systems to keep algorithms in check and develop more accurate predictions.

      “I wanted to take 2.C161 because machine-learning models are usually a “black box,” but this class taught us how to construct a system model that is informed by physics so we can peek inside,” explains Crystal Owens, a mechanical engineering graduate student who took the course in spring 2021.

      As chair of the Committee on the Strategic Integration of Data Science into Mechanical Engineering, Barbastathis has had many conversations with mechanical engineering students, researchers, and faculty to better understand the challenges and successes they’ve had using machine learning in their work.

      “One comment we heard frequently was that these colleagues can see the value of data science methods for problems they are facing in their mechanical engineering-centric research; yet they are lacking the tools to make the most out of it,” says Barbastathis. “Mechanical, civil, electrical, and other types of engineers want a fundamental understanding of data principles without having to convert themselves to being full-time data scientists or AI researchers.”

      Additionally, as mechanical engineering students move on from MIT to their careers, many will need to manage data scientists on their teams someday. Barbastathis hopes to set these students up for success with class 2.C161.

      Bridging MechE and the MIT Schwartzman College of Computing

      Class 2.C161 is part of the MIT Schwartzman College of Computing “Computing Core.” The goal of these classes is to connect data science and physics-based engineering disciplines, like mechanical engineering. Students take the course alongside 6.C402 (Modeling with Machine Learning: from Algorithms to Applications), taught by professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola.

      The two classes are taught concurrently during the semester, exposing students to both fundamentals in machine learning and domain-specific applications in mechanical engineering.

      In 2.C161, Barbastathis highlights how complementary physics-based engineering and data science are. Physical laws present a number of ambiguities and unknowns, ranging from temperature and humidity to electromagnetic forces. Data science can be used to predict these physical phenomena. Meanwhile, having an understanding of physical systems helps ensure the resulting output of an algorithm is accurate and explainable.

      “What’s needed is a deeper combined understanding of the associated physical phenomena and the principles of data science, machine learning in particular, to close the gap,” adds Barbastathis. “By combining data with physical principles, the new revolution in physics-based engineering is relatively immune to the “black box” problem facing other types of machine learning.”

      Equipped with a working knowledge of machine-learning topics covered in class 6.C402 and a deeper understanding of how to pair data science with physics, students are charged with developing a final project that solves for an actual physical system.

      Developing solutions for real-world physical systems

      For their final project, students in 2.C161 are asked to identify a real-world problem that requires data science to address the ambiguity inherent in physical systems. After obtaining all relevant data, students are asked to select a machine-learning method, implement their chosen solution, and present and critique the results.

      Topics this past semester ranged from weather forecasting to the flow of gas in combustion engines, with two student teams drawing inspiration from the ongoing Covid-19 pandemic.

      Owens and her teammates, fellow graduate students Arun Krishnadas and Joshua David John Rathinaraj, set out to develop a model for the Covid-19 vaccine rollout.

      “We developed a method of combining a neural network with a susceptible-infected-recovered (SIR) epidemiological model to create a physics-informed prediction system for the spread of Covid-19 after vaccinations started,” explains Owens.

      The team accounted for various unknowns including population mobility, weather, and political climate. This combined approach resulted in a prediction of Covid-19’s spread during the vaccine rollout that was more reliable than using either the SIR model or a neural network alone.

      Another team, including graduate student Yiwen Hu, developed a model to predict mutation rates in Covid-19, a topic that became all too pertinent as the delta variant began its global spread.

      “We used machine learning to predict the time-series-based mutation rate of Covid-19, and then incorporated that as an independent parameter into the prediction of pandemic dynamics to see if it could help us better predict the trend of the Covid-19 pandemic,” says Hu.

      Hu, who had previously conducted research into how vibrations on coronavirus protein spikes affect infection rates, hopes to apply the physics-based machine-learning approaches he learned in 2.C161 to his research on de novo protein design.

      Whatever the physical system students addressed in their final projects, Barbastathis was careful to stress one unifying goal: the need to assess ethical implications in data science. While more traditional computing methods like face or voice recognition have proven to be rife with ethical issues, there is an opportunity to combine physical systems with machine learning in a fair, ethical way.

      “We must ensure that collection and use of data are carried out equitably and inclusively, respecting the diversity in our society and avoiding well-known problems that computer scientists in the past have run into,” says Barbastathis.

      Barbastathis hopes that by encouraging mechanical engineering students to be both ethics-literate and well-versed in data science, they can move on to develop reliable, ethically sound solutions and predictions for physical-based engineering challenges. More