Monthly Archives: May 2022

Laboratory for Information and Decision Systems (LIDS) student Sarah Cen remembers the lecture that sent her down the track to an upstream question.

At a talk on ethical artificial intelligence, the speaker brought up a variation on the famous trolley problem, which outlines a philosophical choice between two undesirable outcomes.

The speaker’s scenario: Say a self-driving car is traveling down a narrow alley with an elderly woman walking on one side and a small child on the other, and no way to thread between both without a fatality. Who should the car hit?

Then the speaker said: Let’s take a step back. Is this the question we should even be asking?

That’s when things clicked for Cen. Instead of considering the point of impact, a self-driving car could have avoided choosing between two bad outcomes by making a decision earlier on — the speaker pointed out that, when entering the alley, the car could have determined that the space was narrow and slowed to a speed that would keep everyone safe.

Recognizing that today’s AI safety approaches often resemble the trolley problem, focusing on downstream regulation such as liability after someone is left with no good choices, Cen wondered: What if we could design better upstream and downstream safeguards to such problems? This question has informed much of Cen’s work.

“Engineering systems are not divorced from the social systems on which they intervene,” Cen says. Ignoring this fact risks creating tools that fail to be useful when deployed or, more worryingly, that are harmful.

Cen arrived at LIDS in 2018 via a slightly roundabout route. She first got a taste for research during her undergraduate degree at Princeton University, where she majored in mechanical engineering. For her master’s degree, she changed course, working on radar solutions in mobile robotics (primarily for self-driving cars) at Oxford University. There, she developed an interest in AI algorithms, curious about when and why they misbehave. So, she came to MIT and LIDS for her doctoral research, working with Professor Devavrat Shah in the Department of Electrical Engineering and Computer Science, for a stronger theoretical grounding in information systems.

Auditing social media algorithms

Together with Shah and other collaborators, Cen has worked on a wide range of projects during her time at LIDS, many of which tie directly to her interest in the interactions between humans and computational systems. In one such project, Cen studies options for regulating social media. Her recent work provides a method for translating human-readable regulations into implementable audits.

To get a sense of what this means, suppose that regulators require that any public health content — for example, on vaccines — not be vastly different for politically left- and right-leaning users. How should auditors check that a social media platform complies with this regulation? Can a platform be made to comply with the regulation without damaging its bottom line? And how does compliance affect the actual content that users do see?

Designing an auditing procedure is difficult in large part because there are so many stakeholders when it comes to social media. Auditors have to inspect the algorithm without accessing sensitive user data. They also have to work around tricky trade secrets, which can prevent them from getting a close look at the very algorithm that they are auditing because these algorithms are legally protected. Other considerations come into play as well, such as balancing the removal of misinformation with the protection of free speech.

To meet these challenges, Cen and Shah developed an auditing procedure that does not need more than black-box access to the social media algorithm (which respects trade secrets), does not remove content (which avoids issues of censorship), and does not require access to users (which preserves users’ privacy).

In their design process, the team also analyzed the properties of their auditing procedure, finding that it ensures a desirable property they call decision robustness. As good news for the platform, they show that a platform can pass the audit without sacrificing profits. Interestingly, they also found the audit naturally incentivizes the platform to show users diverse content, which is known to help reduce the spread of misinformation, counteract echo chambers, and more.

Who gets good outcomes and who gets bad ones?

In another line of research, Cen looks at whether people can receive good long-term outcomes when they not only compete for resources, but also don’t know upfront what resources are best for them.

Some platforms, such as job-search platforms or ride-sharing apps, are part of what is called a matching market, which uses an algorithm to match one set of individuals (such as workers or riders) with another (such as employers or drivers). In many cases, individuals have matching preferences that they learn through trial and error. In labor markets, for example, workers learn their preferences about what kinds of jobs they want, and employers learn their preferences about the qualifications they seek from workers.

But learning can be disrupted by competition. If workers with a particular background are repeatedly denied jobs in tech because of high competition for tech jobs, for instance, they may never get the knowledge they need to make an informed decision about whether they want to work in tech. Similarly, tech employers may never see and learn what these workers could do if they were hired.

Cen’s work examines this interaction between learning and competition, studying whether it is possible for individuals on both sides of the matching market to walk away happy.

Modeling such matching markets, Cen and Shah found that it is indeed possible to get to a stable outcome (workers aren’t incentivized to leave the matching market), with low regret (workers are happy with their long-term outcomes), fairness (happiness is evenly distributed), and high social welfare.

Interestingly, it’s not obvious that it’s possible to get stability, low regret, fairness, and high social welfare simultaneously.  So another important aspect of the research was uncovering when it is possible to achieve all four criteria at once and exploring the implications of those conditions.

What is the effect of X on Y?

For the next few years, though, Cen plans to work on a new project, studying how to quantify the effect of an action X on an outcome Y when it’s expensive — or impossible — to measure this effect, focusing in particular on systems that have complex social behaviors.

For instance, when Covid-19 cases surged in the pandemic, many cities had to decide what restrictions to adopt, such as mask mandates, business closures, or stay-home orders. They had to act fast and balance public health with community and business needs, public spending, and a host of other considerations.

Typically, in order to estimate the effect of restrictions on the rate of infection, one might compare the rates of infection in areas that underwent different interventions. If one county has a mask mandate while its neighboring county does not, one might think comparing the counties’ infection rates would reveal the effectiveness of mask mandates. 

But of course, no county exists in a vacuum. If, for instance, people from both counties gather to watch a football game in the maskless county every week, people from both counties mix. These complex interactions matter, and Sarah plans to study questions of cause and effect in such settings.

“We’re interested in how decisions or interventions affect an outcome of interest, such as how criminal justice reform affects incarceration rates or how an ad campaign might change the public’s behaviors,” Cen says.

Cen has also applied the principles of promoting inclusivity to her work in the MIT community.

As one of three co-presidents of the Graduate Women in MIT EECS student group, she helped organize the inaugural GW6 research summit featuring the research of women graduate students — not only to showcase positive role models to students, but also to highlight the many successful graduate women at MIT who are not to be underestimated.

Whether in computing or in the community, a system taking steps to address bias is one that enjoys legitimacy and trust, Cen says. “Accountability, legitimacy, trust — these principles play crucial roles in society and, ultimately, will determine which systems endure with time.” 

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Even as rapid improvements in artificial intelligence have led to speculation over significant changes in the health care landscape, the adoption of AI in health care has been minimal. A 2020 survey by Brookings, for example, found that less than 1 percent of job postings in health care required AI-related skills.

The Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), a research center within the MIT Schwarzman College of Computing, recently hosted the MITxMGB AI Cures Conference in an effort to accelerate the adoption of clinical AI tools by creating new opportunities for collaboration between researchers and physicians focused on improving care for diverse patient populations.

Once virtual, the AI Cures Conference returned to in-person attendance at MIT’s Samberg Conference Center on the morning of April 25, welcoming over 300 attendees primarily made up of researchers and physicians from MIT and Mass General Brigham (MGB). 

MIT President L. Rafael Reif began the event by welcoming attendees and speaking to the “transformative capacity of artificial intelligence and its ability to detect, in a dark river of swirling data, the brilliant patterns of meaning that we could never see otherwise.” MGB’s president and CEO Anne Klibanski followed up by lauding the joint partnership between the two institutions and noting that the collaboration could “have a real impact on patients’ lives” and “help to eliminate some of the barriers to information-sharing.”

Domestically, about $20 million in subcontract work currently takes place between MIT and MGB. MGB’s chief academic officer and AI Cures co-chair Ravi Thadhani thinks that five times that amount would be necessary in order to do more transformative work. “We could certainly be doing more,” Thadhani said. “The conference … just scratched the surface of a relationship between a leading university and a leading health-care system.”

MIT Professor and AI Cures Co-Chair Regina Barzilay echoed similar sentiments during the conference. “If we’re going to take 30 years to take all the algorithms and translate them into patient care, we’ll be losing patient lives,” she said. “I hope the main impact of this conference is finding a way to translate it into a clinical setting to benefit patients.”

This year’s event featured 25 speakers and two panels, with many of the speakers addressing the obstacles facing the mainstream deployment of AI in clinical settings, from fairness and clinical validation to regulatory hurdles and translation issues using AI tools. 

On the speaker list, of note was the appearance of Amir Khan, a senior fellow from the U.S. Food and Drug Administration (FDA), who fielded a number of questions from curious researchers and clinicians on the FDA’s ongoing efforts and challenges in regulating AI in health care.

The conference also covered many of the impressive advancements AI made in the past several years: Lecia Sequist, a lung cancer oncologist from MGB, spoke about her collaborative work with MGB radiologist Florian Fintelmann and Barzilay to develop an AI algorithm that could detect lung cancer up to six years in advance. MIT Professor Dina Katabi presented with MGB’s doctors Ipsit Vahia and Aleksandar Videnovic on an AI device that could detect the presence of Parkinson’s disease simply by monitoring a person’s breathing patterns while asleep. “It is an honor to collaborate with Professor Katabi,” Videnovic said during the presentation.

MIT Assistant Professor Marzyeh Ghassemi, whose presentation concerned designing machine learning processes for more equitable health systems, found the longer-range perspectives shared by the speakers during the first panel on AI changing clinical science compelling.

“What I really liked about that panel was the emphasis on how relevant technology and AI has become in clinical science,” Ghassemi says. “You heard some panel members [Eliezer Van Allen, Najat Khan, Isaac Kohane, Peter Szolovits] say that they used to be the only person at a conference from their university that was focused on AI and ML [machine learning], and now we’re in a space where we have a miniature conference with posters just with people from MIT.”

The 88 posters accepted to AI Cures were on display for attendees to peruse during the lunch break. The presented research spanned different areas of focus from clinical AI and AI for biology to AI-powered systems and others. 

“I was really impressed with the breadth of work going on in this space,” Collin Stultz, a professor at MIT, says. Stultz also spoke at AI Cures, focusing primarily on the risks of interpretability and explainability when using AI tools in a clinical setting, using cardiovascular care as an example of showing how algorithms could potentially mislead clinicians with grave consequences for patients. 

“There are a growing number of failures in this space where companies or algorithms strive to be the most accurate, but do not take into consideration how the clinician views the algorithm and their likelihood of using it,” Stultz said. “This is about what the patient deserves and how the clinician is able to explain and justify their decision-making to the patient.” 

Phil Sharp, MIT Institute Professor and chair of the advisory board for Jameel Clinic, found the conference energizing and thought that the in-person interactions were crucial to gaining insight and motivation, unmatched by many conferences that are still being hosted virtually. 

“The broad participation by students and leaders and members of the community indicate that there’s an awareness that this is a tremendous opportunity and a tremendous need,” Sharp says. He pointed out that AI and machine learning are being used to predict the structures of “almost everything” from protein structures to drug efficacy. “It says to young people, watch out, there might be a machine revolution coming.” 

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